Systems and methods to select locations of interest based on distance from route points or route paths

ABSTRACT

Selection of offers, locations or merchants based on their distances from a portion of a user&#39;s route and their affinity for the user is disclosed. A route employed by a user for travelling to a destination is obtained and segmented at identified vertices. Locations that are proximate to the route segments are determined. The proximate locations are further analyzed to obtain their specific distances from a selected portion such as the user&#39;s origin or destination or any route segment. Location based services are associated with those proximate locations that are closest to the selected portion on the user&#39;s route or those with the better utility.

RELATED APPLICATIONS

The present application is a continuation application of U.S. patentapplication Ser. No. 14/163,877, filed Jan. 24, 2014 and entitled“Systems and Methods to Select Locations of Interest based on Distancefrom Route Points or Route Paths”, which claims priority to Prov. U.S.Pat. App. Ser. No. 61/756,860 filed Jan. 25, 2013 and entitled “Systemsand Methods to Select Locations of Interest based on Distance from RoutePoints”, and Prov. U.S. Pat. App. Ser. No. 61/756,858 filed Jan. 25,2013 and entitled “Systems and Methods to Select Locations of Interestbased on Distance from Route Paths”, the entire disclosures of whichapplications are hereby incorporated herein by reference.

The present application relates to U.S. patent application Ser. No.14/075,518, filed Nov. 8, 2013, published as U.S. Pat. App. Pub. No.2014/0136104, and entitled “Systems and Methods for Route Prediction”,and U.S. patent application Ser. No. 14/106,018, filed Dec. 13, 2013,published as U.S. Pat. App. Pub. No. 2014/0172576, and entitled “Systemsand Methods to Select Locations of Interest”, the entire disclosures ofwhich applications are incorporated herein by reference.

FIELD OF THE TECHNOLOGY

At least some embodiments of the present disclosure relate to theselection of offers or locations based on their distance to an origin, adestination or a route path of a predicted or actual route and theiraffinity for a user.

BACKGROUND

Millions of transactions occur daily through the use of payment cards,such as credit cards, debit cards, prepaid cards, etc. Correspondingrecords of the transactions are recorded in databases for settlement andfinancial recordkeeping (e.g., to meet the requirements of governmentregulations). Such data can be mined and analyzed for trends,statistics, and other analyses. Sometimes such data are mined forspecific advertising goals, such as to provide targeted offers toaccount holders, as described in PCT Pub. No. WO 2008/067543 A2,published on Jun. 5, 2008 and entitled “Techniques for Targeted Offers.”

U.S. Pat. App. Pub. No. 2009/0216579, published on Aug. 27, 2009 andentitled “Tracking Online Advertising using Payment Services,” disclosesa system in which a payment service identifies the activity of a userusing a payment card as corresponding with an offer associated with anonline advertisement presented to the user.

U.S. Pat. No. 6,298,330, issued on Oct. 2, 2001 and entitled“Communicating with a Computer Based on the Offline Purchase History ofa Particular Consumer,” discloses a system in which a targetedadvertisement is delivered to a computer in response to receiving anidentifier, such as a cookie, corresponding to the computer.

U.S. Pat. No. 7,035,855, issued on Apr. 25, 2006 and entitled “Processand System for Integrating Information from Disparate Databases forPurposes of Predicting Consumer Behavior,” discloses a system in whichconsumer transactional information is used for predicting consumerbehavior.

U.S. Pat. No. 6,505,168, issued on Jan. 7, 2003 and entitled “System andMethod for Gathering and Standardizing Customer Purchase Information forTarget Marketing,” discloses a system in which categories andsub-categories are used to organize purchasing information by creditcards, debit cards, checks and the like. The customer purchaseinformation is used to generate customer preference information formaking targeted offers.

U.S. Pat. No. 7,444,658, issued on Oct. 28, 2008 and entitled “Methodand System to Perform Content Targeting,” discloses a system in whichadvertisements are selected to be sent to users based on a userclassification performed using credit card purchasing data.

U.S. Pat. App. Pub. No. 2005/0055275, published on Mar. 10, 2005 andentitled “System and Method for Analyzing Marketing Efforts,” disclosesa system that evaluates the cause and effect of advertising andmarketing programs using card transaction data.

U.S. Pat. App. Pub. No. 2008/0217397, published on Sep. 11, 2008 andentitled “Real-Time Awards Determinations,” discloses a system forfacilitating transactions with real-time awards determinations for acardholder, in which the award may be provided to the cardholder as acredit on the cardholder's statement.

The disclosures of the above discussed patent documents are herebyincorporated herein by reference.

BRIEF DESCRIPTION OF THE DRAWINGS

The embodiments are illustrated by way of example and not limitation inthe figures of the accompanying drawings in which like referencesindicate similar elements.

FIG. 1 illustrates a system to provide services based on transactiondata according to one embodiment.

FIG. 2 illustrates the generation of an aggregated spending profileaccording to one embodiment.

FIG. 3 shows a method to generate an aggregated spending profileaccording to one embodiment.

FIG. 4 shows a system to provide information based on transaction dataaccording to one embodiment.

FIG. 5 illustrates a transaction terminal according to one embodiment.

FIG. 6 illustrates an account identifying device according to oneembodiment.

FIG. 7 illustrates a data processing system according to one embodiment.

FIG. 8 shows the structure of account data for providing loyaltyprograms according to one embodiment.

FIG. 9 shows a system to provide real-time messages according to oneembodiment.

FIG. 10 shows a method to provide real-time messages according to oneembodiment.

FIG. 11 shows a schematic diagram wherein a location provider interactswith a route provider associated with a user in accordance with anembodiment.

FIG. 12a shows a schematic diagram of a route predictor in accordancewith an embodiment.

FIG. 12b shows a schematic diagram of a location provider in accordancewith an embodiment.

FIG. 13 shows a method of predicting a route and updating the routedictionary.

FIG. 14a is a map showing various routes to frequent destinations of auser who employs a routing device or a route provider.

FIG. 14b is an illustration showing further analysis of the route by theroute analyzer.

FIG. 15 is another illustration showing further analysis of the selectedroute (1302) by the route analyzer according to one embodiment.

FIG. 16 shows a map wherein radii of map vertices are weighed to ensurethat the entire area that falls within a predetermined route thresholdaccording to one embodiment.

FIG. 17 illustrates the values of the weighted radius for each of thesegments between the vertices of the selected route calculated accordingto one embodiment.

FIG. 18 illustrates further analysis of the selected route wherein twocircles are drawn for each of the route vertices according to oneembodiment.

FIG. 19 illustrates further analysis of the selected route wherein areasurrounding the selected route based on the maximum weighted radii fromthe vertices is obtained in accordance with an embodiment of the presentdisclosure.

FIG. 20 illustrates the area around the route that is selected forfurther analysis based on the contiguous vertices method in accordancewith an embodiment of the present disclosure.

FIG. 21 illustrates the area around the route that is selected forfurther analysis to identify locations of interest based on weightedradii from mid points according to one embodiment.

FIG. 22 illustrates an example wherein a selected route is characterizedby irregular angles according to one embodiment.

FIG. 23 illustrates an embodiment weighted radii from mid-points ofroute segments are used when analyzing the selected route.

FIG. 24 shows compensating for the loss of map area coverage on one sideof the selected route in accordance with embodiments described herein.

FIG. 25a shows one embodiment of a method to adjust the size of theradius in order to cover the map area around the selected routeaccording to one embodiment.

FIG. 25b shows one embodiment of a method to reduce search size bycreating sub-segments and “sub-vertices” at each sub-segment.

FIG. 25c shows one embodiment of a method to reduce search size bycreating sub-segments.

FIG. 26 shows an embodiment for calculation of a weighted radius that isbased on the angle at a vertex.

FIGS. 27a, 27b, 27c, 27d and 27e illustrate various possible angles forthe selected route.

FIG. 28 illustrates a map wherein the convenience store, a first gasstation and a second gas station are identified as candidates to beconsidered as the locations of interest in accordance with oneembodiment.

FIG. 29 illustrates an embodiment related to further determinations ofproximity for the candidate locations in accordance with one embodiment.

FIG. 30 shows a flow chart that details a method of determining thelocations of interest to the user in accordance with one embodiment.

FIG. 31 shows a map including the route illustrating estimation ofutility values in accordance with an embodiment.

FIG. 32 shows a flow chart that details a method of determining thelocations of interest to the user in accordance with one embodiment.

FIG. 33 shows a map including the route illustrating estimation ofutility values in accordance with an embodiment.

FIG. 34 shows a method to provide benefits according to one embodiment.

DETAILED DESCRIPTION Introduction

Many location based services are provided via real-time messaging.Loyalty programs monitor user habits and provide services in real-timethat are relevant to the current user context. For example, when a userstops at specific location, coupons or discounts related to retaillocations at the user's current locale can be provided to the user inreal-time. However, such services tend to be more reactive and are notvery proactive. These services are operative only when the user isactually situated in a particular location or if it is known with greataccuracy that the user will be at the particular location. They do notoperate to proactively draw the user's attention or encourage the userto visit a particular location that may be close to the user's route butwhich the user may not intend to visit. Hence, systems which monitor acurrent route being traversed by a user and which predict locations ofinterest that are along the user's current route and within apredetermined threshold distance from the user's current route need canbe used to attract users to specific locations. The systems whichmonitor a user's current routes encourage the user to visit locationswhich the user did not initially intend to visit as they are configuredto pick those locations that are at are proximate from the user'scurrent route or are otherwise suited to the user's current context. Incertain embodiments, the user's intentions can be derived by monitoringthe user's current route settings or predicting the user's route byaccessing contextual information from real-time or archived sources.

When the locations of interest are determined, non-transaction data canbe used to send real-time messages pertaining to the predicted locationsof interest. By employing knowledge of a predicted route or a routeselected for traversal by a user, locations of interest to the useralong the selected route can be determined and location based servicestargeted to such predicted locations can be advertised to the user. Inan embodiment, the locations of interest can be predicted by predictingthe likely route of the user. In an embodiment, further filtering of thelocations of interest can be achieved by weighing the distances withuser specific data such as a user's affinity for particular vendors ormerchants or locations.

Typical shopping patterns for users show a predisposition to selectmerchants based on their proximity to points along a route combined withthe affinity for particular offers. Various embodiments disclosed hereinpertain to selecting offers and merchants along a user's route both withand without affinities attached to them. The offers or merchants can beselected based on their distances to the origin or destination of aroute and the affinity. The user, therefore, does not have to deviatefar from their current route to visit the associated locations and hencemay be motivated to capitalize on the location based services.

The transaction data, such as records of transactions made via creditaccounts, debit accounts, prepaid accounts, bank accounts, stored valueaccounts and the like, can be further processed to optionally provideinformation for various services, such as reporting, benchmarking,advertising, content or offer selection, customization, personalization,prioritization, etc. In one embodiment of improving privacy protections,users are required to enroll in a service program and provide consent toallow the system to use related transaction data and/or other data forthe related services, and the system is configured to provide theservices while protecting the privacy of the users in accordance withthe enrollment agreement and user consent.

For example, based on the transaction data, an advertising network inone embodiment is provided to present personalized or targetedadvertisements/offers on behalf of advertisers. A computing apparatusof, or associated with, the transaction handler uses the transactiondata and/or other data, such as account data, merchant data, searchdata, social networking data, web data, etc., to develop intelligenceinformation about individual customers, or certain types or groups ofcustomers. The intelligence information can be used to select, identify,generate, adjust, prioritize, and/or personalize advertisements/offersto the customers. The transaction handler may be further automated toprocess the advertisement fees charged to the advertisers, using theaccounts of the advertisers, in response to the advertising activities.

For example, the computing apparatus can be configured to generatetrigger records for a transaction handler to identify authorizationrequests that satisfy the conditions specified in the trigger records,identify communication references of the users associated with theidentified authorization requests, and use the communication referencesto target real-time messages at the users in parallel with thetransaction handler providing responses to the respective authorizationrequests. Details in one embodiment regarding the generation anddelivery of messages in real-time with the processing of transactionsare provided in the section entitled “REAL-TIME MESSAGES.”

For example, the computing apparatus can be programmable for real-timeinteraction with users to provide messages and/or offers, validatefulfillment conditions, and provide benefits to qualified users tofulfill the offers. In one embodiment, the computing apparatus isconfigured to be programmed via accepting definitions of independentevents and linking the events via prerequisite requirements to specifyqualification conditions. The linked events form a flow or network ofevents; and user progress in the flow or network of events is tracked.The operations for each event are performed in an atomic way to allowthe user positions in the flow or network of events to be identified asbeing in between adjacent events in the network. As a result, theprogramming of the real-time interaction, including the offer rules andmessages, can be easily modified during the execution of theprogramming. Details in one embodiment regarding the formulation andmanagement of real-time interaction are provided in the section entitled“RULE FORMATION AND MANAGEMENT.”

For example, the computing apparatus can be configured to allow a userto use any of a plurality of registered accounts to participate in anoffer campaign, such as performing transactions in the registeredaccounts to fulfill requirements to obtain the benefit of the offercampaign. In one embodiment, the offer campaign is programmed by offerrules that identify the real time interactions with the user in responseto the actions of the user, such as transactions made using any of theregistered accounts of the user. The offer campaign for the user isdriven at least in part by the actions of the user, such as thetransactions made by the user. In one embodiment, transactions in theregistered accounts of the user jointly advances the offer campaign forthe user; and a milestone achieved in the offer campaign using oneaccount of the user is recognized as a milestone achieved by the userwith respect to the multiple registered accounts. Thus, the offercampaign for the user can be advanced by the user via differentaccounts, as if the registered accounts were a same account; and theuser is not limited to using a particular account to participate in theoffer campaign, nor using different accounts to drive the offer campaignseparately, as if the accounts were assigned to different users. Detailsin one embodiment regarding the configuration of real time interactionsusing multiple accounts of a user are provided in the section entitled“MULTIPLE ACCOUNTS.”

In one embodiment, the computing apparatus is configured to target thesame offer differently to users based on the media channels used todeliver the offer. An offer can be configured to include firstqualification conditions formulated based on triggering events, such asthe current location of a user, the current transaction of the user asbeing processed by a transaction handler, and second qualificationconditions not based on such triggering events. To users reachable via afirst set of media channels, the first qualification conditions areignored in selecting candidate users for the delivery of the offer; andthe candidate users are selected based on the second qualificationconditions. If the offer has not be delivered to a user via the firstset of media channels, the computing apparatus is configured to deliverthe offer to the user via a second set of media channels, when the usersatisfies both the first qualification conditions and the secondqualification conditions. Details in one embodiment are provided in thesection entitled “MULTIPLE MEDIA CHANNELS.”

In one embodiment, a system and method is configured to allow an offercampaign to be specified based on requirements of transactions withmultiple merchants. Details in one embodiment are provided in thesection entitled “MULTIPLE MERCHANTS.”

In one embodiment, the computing apparatus correlates transactions withactivities that occurred outside the context of the transaction, such asonline advertisements presented to the customers that at least in partcause offline transactions. The correlation data can be used todemonstrate the success of the advertisements, and/or to improveintelligence information about how individual customers and/or varioustypes or groups of customers respond to the advertisements.

In one embodiment, the computing apparatus correlates, or providesinformation to facilitate the correlation of, transactions with onlineactivities of the customers, such as searching, web browsing, socialnetworking and consuming advertisements, with other activities, such aswatching television programs, and/or with events, such as meetings,announcements, natural disasters, accidents, news announcements, etc.

In one embodiment, the correlation results are used in predictive modelsto predict transactions and/or spending patterns based on activities orevents, to predict activities or events based on transactions orspending patterns, to provide alerts or reports, etc.

In one embodiment, a single entity operating the transaction handlerperforms various operations in the services provided based on thetransaction data. For example, in the presentation of the personalizedor targeted advertisements, the single entity may perform the operationssuch as generating the intelligence information, selecting relevantintelligence information for a given audience, selecting, identifying,adjusting, prioritizing, personalizing and/or generating advertisementsbased on selected relevant intelligence information, and facilitatingthe delivery of personalized or targeted advertisements, etc.Alternatively, the entity operating the transaction handler cooperateswith one or more other entities by providing information to theseentities to allow these entities to perform at least some of theoperations for presentation of the personalized or targetedadvertisements.

Transaction Data Based Services

FIG. 1 illustrates a system to provide services based on transactiondata according to one embodiment. In FIG. 1, the system includes atransaction terminal (105) to initiate financial transactions for a user(101), a transaction handler (103) to generate transaction data (109)from processing the financial transactions of the user (101) (and thefinancial transactions of other users), a profile generator (121) togenerate transaction profiles (127) based on the transaction data (109)to provide information/intelligence about user preferences and spendingpatterns, a point of interaction (107) to provide information and/oroffers to the user (101), a user tracker (113) to generate user data(125) to identify the user (101) using the point of interaction (107), aprofile selector (129) to select a profile (131) specific to the user(101) identified by the user data (125), and an advertisement selector(133) to select, identify, generate, adjust, prioritize and/orpersonalize advertisements for presentation to the user (101) on thepoint of interaction (107) via a media controller (115).

In FIG. 1, the system further includes a correlator (117) to correlateuser specific advertisement data (119) with transactions resulting fromthe user specific advertisement data (119). The correlation results(123) can be used by the profile generator (121) to improve thetransaction profiles (127).

The transaction profiles (127) of one embodiment are generated from thetransaction data (109) in a way as illustrated in FIGS. 2 and 3. Forexample, in FIG. 2, an aggregated spending profile (341) is generatedvia the factor analysis (327) and cluster analysis (329) to summarize(335) the spending patterns/behaviors reflected in the transactionrecords (301).

In one embodiment, a data warehouse (149) as illustrated in FIG. 4 iscoupled with the transaction handler (103) to store the transaction data(109) and other data, such as account data (111), transaction profiles(127) and correlation results (123). In FIG. 4, a portal (143) iscoupled with the data warehouse (149) to provide data or informationderived from the transaction data (109), in response to a query requestfrom a third party or as an alert or notification message.

In FIG. 4, the transaction handler (103) is coupled between an issuerprocessor (145) in control of a consumer account (146) and an acquirerprocessor (147) in control of a merchant account (148). An accountidentification device (141) is configured to carry the accountinformation (142) that identifies the consumer account (146) with theissuer processor (145) and provide the account information (142) to thetransaction terminal (105) of a merchant to initiate a transactionbetween the user (101) and the merchant.

FIGS. 5 and 6 illustrate examples of transaction terminals (105) andaccount identification devices (141). FIG. 7 illustrates the structureof a data processing system (170) that can be used to implement, withmore or fewer elements, at least some of the components in the system,such as the point of interaction (107), the transaction handler (103),the portal (143), the data warehouse, the account identification device(141), the transaction terminal (105), the user tracker (113), theprofile generator (121), the profile selector (129), the advertisementselector (133), the media controller (115), etc. Some embodiments usemore or fewer components than those illustrated, such as, in FIGS. 1,4-7, and other figures, as further discussed in the section entitled“VARIATIONS.”

In one embodiment, the transaction data (109) relates to financialtransactions processed by the transaction handler (103); and the accountdata (111) relates to information about the account holders involved inthe transactions. Further data, such as merchant data that relates tothe location, business, products and/or services of the merchants thatreceive payments from account holders for their purchases, can be usedin the generation of the transaction profiles (127, 341).

In one embodiment, the financial transactions are made via an accountidentification device (141), such as financial transaction cards (e.g.,credit cards, debit cards, banking cards, etc.); the financialtransaction cards may be embodied in various devices, such as plasticcards, chips, radio frequency identification (RFID) devices, mobilephones, personal digital assistants (PDAs), etc.; and the financialtransaction cards may be represented by account identifiers (e.g.,account numbers or aliases). In one embodiment, the financialtransactions are made via directly using the account information (142),without physically presenting the account identification device (141).

Further features, modifications and details are provided in varioussections of this description.

Centralized Data Warehouse

In one embodiment, the transaction handler (103) couples with acentralized data warehouse (149) organized around the transaction data(109). For example, the centralized data warehouse (149) may include,and/or support the determination of, spend band distribution,transaction count and amount, merchant categories, merchant by state,cardholder segmentation by velocity scores, and spending within merchanttarget, competitive set and cross-section. For example, the centralizeddata warehouse (149) may include the advertisement data (135) and/oroffers of benefits such as discount, reward, points, cashback, etc. Theoffers can be communicated to the users (e.g., 101) via theadvertisement data (135) or as part of the advertisement data (135).

In one embodiment, the centralized data warehouse (149) providescentralized management but allows decentralized execution. For example,a third party strategic marketing analyst, statistician, marketer,promoter, business leader, etc., may access the centralized datawarehouse (149) to analyze customer and shopper data, to providefollow-up analyses of customer contributions, to develop propensitymodels for increased conversion of marketing campaigns, to developsegmentation models for marketing, etc. The centralized data warehouse(149) can be used to manage advertisement campaigns and analyze responseprofitability.

In one embodiment, the centralized data warehouse (149) includesmerchant data (e.g., data about sellers), customer/business data (e.g.,data about buyers), and transaction records (301) between sellers andbuyers over time. The centralized data warehouse (149) can be used tosupport corporate sales forecasting, fraud analysis reporting,sales/customer relationship management (CRM) business intelligence,credit risk prediction and analysis, advanced authorization reporting,merchant benchmarking, business intelligence for small business,rewards, etc.

In one embodiment, the transaction data (109) is combined with externaldata, such as surveys, benchmarks, search engine statistics,demographics, competition information, emails, etc., to flag key eventsand data values, to set customer, merchant, data or event triggers, andto drive new transactions and new customer contacts.

Transaction Profile

In FIG. 1, the profile generator (121) generates transaction profiles(127) based on the transaction data (109), the account data (111),and/or other data, such as non-transactional data, wish lists, merchantprovided information, address information, information from socialnetwork websites, information from credit bureaus, information fromsearch engines, and other examples discussed in U.S. patent applicationSer. No. 12/614,603, filed Nov. 9, 2009, assigned U.S. Pat. App. Pub.No. 2011/0054981, and entitled “Analyzing Local Non-Transactional Datawith Transactional Data in Predictive Models,” the disclosure of whichis hereby incorporated herein by reference.

In one embodiment, the transaction profiles (127) provide intelligenceinformation on the behavior, pattern, preference, propensity, tendency,frequency, trend, and budget of the user (101) in making purchases. Inone embodiment, the transaction profiles (127) include information aboutwhat the user (101) owns, such as points, miles, or other rewardscurrency, available credit, and received offers, such as coupons loadedinto the accounts of the user (101). In one embodiment, the transactionprofiles (127) include information based on past offer/coupon redemptionpatterns. In one embodiment, the transaction profiles (127) includeinformation on shopping patterns in retail stores as well as online,including frequency of shopping, amount spent in each shopping trip,distance of merchant location (retail) from the address of the accountholder(s), etc.

In one embodiment, the transaction handler (103) (and/or the portal(143)) is configured to provide at least part of the intelligence forthe prioritization, generation, selection, customization and/oradjustment of the advertisement for delivery within a transactionprocess involving the transaction handler (103). For example, theadvertisement may be presented to a customer in response to the customermaking a payment via the transaction handler (103).

Some of the transaction profiles (127) are specific to the user (101),or to an account of the user (101), or to a group of users of which theuser (101) is a member, such as a household, family, company,neighborhood, city, or group identified by certain characteristicsrelated to online activities, offline purchase activities, merchantpropensity, etc.

The profile generator (121) may generate and update the transactionprofiles (127) in batch mode periodically, or generates the transactionprofiles (127) in real time, or just in time, in response to a requestreceived in the portal (143) for such profiles.

The transaction profiles (127) of one embodiment include the values fora set of parameters. Computing the values of the parameters may involvecounting transactions that meet one or more criteria, and/or building astatistically-based model in which one or more calculated values ortransformed values are put into a statistical algorithm that weightseach value to optimize its collective predictiveness for variouspredetermined purposes.

Further details and examples about the transaction profiles (127) in oneembodiment are provided in the section entitled “AGGREGATED SPENDINGPROFILE.”

Non-Transactional Data

In one embodiment, the transaction data (109) is analyzed in connectionwith non-transactional data to generate transaction profiles (127)and/or to make predictive models.

In one embodiment, transactions are correlated with non-transactionalevents, such as news, conferences, shows, announcements, market changes,natural disasters, etc. to establish cause and effect relations topredict future transactions or spending patterns. For example,non-transactional data may include the geographic location of a newsevent, the date of an event from an events calendar, the name of aperformer for an upcoming concert, etc. The non-transactional data canbe obtained from various sources, such as newspapers, websites, blogs,social networking sites, etc.

When the cause and effect relationships between the transactions andnon-transactional events are known (e.g., based on prior researchresults, domain knowledge, expertise), the relationships can be used inpredictive models to predict future transactions or spending patterns,based on events that occurred recently or are happening in real time.

In one embodiment, the non-transactional data relates to events thathappened in a geographical area local to the user (101) that performedthe respective transactions. In one embodiment, a geographical area islocal to the user (101) when the distance from the user (101) tolocations in the geographical area is within a convenient range fordaily or regular travel, such as 20, 50 or 100 miles from an address ofthe user (101), or within the same city or zip code area of an addressof the user (101). Examples of analyses of local non-transactional datain connection with transaction data (109) in one embodiment are providedin U.S. patent application Ser. No. 12/614,603, filed Nov. 9, 2009,assigned U.S. Pat. App. Pub. No. 2011/0054981, and entitled “AnalyzingLocal Non-Transactional Data with Transactional Data in PredictiveModels,” the disclosure of which is hereby incorporated herein byreference.

In one embodiment, the non-transactional data is not limited to localnon-transactional data. For example, national non-transactional data canalso be used.

In one embodiment, the transaction records (301) are analyzed infrequency domain to identify periodic features in spending events. Theperiodic features in the past transaction records (301) can be used topredict the probability of a time window in which a similar transactionwould occur. For example, the analysis of the transaction data (109) canbe used to predict when a next transaction having the periodic featurewould occur, with which merchant, the probability of a repeatedtransaction with a certain amount, the probability of exception, theopportunity to provide an advertisement or offer such as a coupon, etc.In one embodiment, the periodic features are detected through countingthe number of occurrences of pairs of transactions that occurred withina set of predetermined time intervals and separating the transactionpairs based on the time intervals. Some examples and techniques for theprediction of future transactions based on the detection of periodicfeatures in one embodiment are provided in U.S. patent application Ser.No. 12/773,770, filed May 4, 2010, assigned U.S. Pat. App. Pub. No.2010/0280882, and entitled “Frequency-Based Transaction Prediction andProcessing,” the disclosure of which is hereby incorporated herein byreference.

Techniques and details of predictive modeling in one embodiment areprovided in U.S. Pat. Nos. 6,119,103, 6,018,723, 6,658,393, 6,598,030,and 7,227,950, the disclosures of which are hereby incorporated hereinby reference.

In one embodiment, offers are based on the point-of-service to offereedistance to allow the user (101) to obtain in-person services. In oneembodiment, the offers are selected based on transaction history andshopping patterns in the transaction data (109) and/or the distancebetween the user (101) and the merchant. In one embodiment, offers areprovided in response to a request from the user (101), or in response toa detection of the location of the user (101). Examples and details ofat least one embodiment are provided in U.S. patent application Ser. No.11/767,218, filed Jun. 22, 2007, assigned U.S. Pat. App. Pub. No.2008/0319843, and entitled “Supply of Requested Offer Based on Point-ofService to Offeree Distance,” U.S. patent application Ser. No.11/755,575, filed May 30, 2007, assigned U.S. Pat. App. Pub. No.2008/0300973, and entitled “Supply of Requested Offer Based on OffereeTransaction History,” U.S. patent application Ser. No. 11/855,042, filedSep. 13, 2007, assigned U.S. Pat. App. Pub. No. 2009/0076896, andentitled “Merchant Supplied Offer to a Consumer within a PredeterminedDistance,” U.S. patent application Ser. No. 11/855,069, filed Sep. 13,2007, assigned U.S. Pat. App. Pub. No. 2009/0076925, and entitled“Offeree Requested Offer Based on Point-of Service to Offeree Distance,”and U.S. patent application Ser. No. 12/428,302, filed Apr. 22, 2009,assigned U.S. Pat. App. Pub. No. 2010/0274627, and entitled “Receivingan Announcement Triggered by Location Data,” the disclosures of whichapplications are hereby incorporated herein by reference.

Targeting Advertisement

In FIG. 1, an advertisement selector (133) prioritizes, generates,selects, adjusts, and/or customizes the available advertisement data(135) to provide user specific advertisement data (119) based at leastin part on the user specific profile (131). The advertisement selector(133) uses the user specific profile (131) as a filter and/or a set ofcriteria to generate, identify, select and/or prioritize advertisementdata for the user (101). A media controller (115) delivers the userspecific advertisement data (119) to the point of interaction (107) forpresentation to the user (101) as the targeted and/or personalizedadvertisement.

In one embodiment, the user data (125) includes the characterization ofthe context at the point of interaction (107). Thus, the use of the userspecific profile (131), selected using the user data (125), includes theconsideration of the context at the point of interaction (107) inselecting the user specific advertisement data (119).

In one embodiment, in selecting the user specific advertisement data(119), the advertisement selector (133) uses not only the user specificprofile (131), but also information regarding the context at the pointof interaction (107). For example, in one embodiment, the user data(125) includes information regarding the context at the point ofinteraction (107); and the advertisement selector (133) explicitly usesthe context information in the generation or selection of the userspecific advertisement data (119).

In one embodiment, the advertisement selector (133) may query forspecific information regarding the user (101) before providing the userspecific advertisement data (119). The queries may be communicated tothe operator of the transaction handler (103) and, in particular, to thetransaction handler (103) or the profile generator (121). For example,the queries from the advertisement selector (133) may be transmitted andreceived in accordance with an application programming interface orother query interface of the transaction handler (103), the profilegenerator (121) or the portal (143) of the transaction handler (103).

In one embodiment, the queries communicated from the advertisementselector (133) may request intelligence information regarding the user(101) at any level of specificity (e.g., segment level, individuallevel). For example, the queries may include a request for a certainfield or type of information in a cardholder's aggregate spendingprofile (341). As another example, the queries may include a request forthe spending level of the user (101) in a certain merchant category overa prior time period (e.g., six months).

In one embodiment, the advertisement selector (133) is operated by anentity that is separate from the entity that operates the transactionhandler (103). For example, the advertisement selector (133) may beoperated by a search engine, a publisher, an advertiser, an ad network,or an online merchant. The user specific profile (131) is provided tothe advertisement selector (133) to assist the customization of the userspecific advertisement data (119).

In one embodiment, advertising is targeted based on shopping patterns ina merchant category (e.g., as represented by a Merchant Category Code(MCC)) that has high correlation of spending propensity with othermerchant categories (e.g., other MCCs). For example, in the context of afirst MCC for a targeted audience, a profile identifying second MCCsthat have high correlation of spending propensity with the first MCC canbe used to select advertisements for the targeted audience.

In one embodiment, the aggregated spending profile (341) is used toprovide intelligence information about the spending patterns,preferences, and/or trends of the user (101). For example, a predictivemodel can be established based on the aggregated spending profile (341)to estimate the needs of the user (101). For example, the factor values(344) and/or the cluster ID (343) in the aggregated spending profile(341) can be used to determine the spending preferences of the user(101). For example, the channel distribution (345) in the aggregatedspending profile (341) can be used to provide a customized offertargeted for a particular channel, based on the spending patterns of theuser (101).

In one embodiment, mobile advertisements, such as offers and coupons,are generated and disseminated based on aspects of prior purchases, suchas timing, location, and nature of the purchases, etc. In oneembodiment, the size of the benefit of the offer or coupon is based onpurchase volume or spending amount of the prior purchase and/or thesubsequent purchase that may qualify for the redemption of the offer.Further details and examples of one embodiment are provided in U.S.patent application Ser. No. 11/960,162, filed Dec. 19, 2007, assignedU.S. Pat. App. Pub. No. 2008/0201226, and entitled “Mobile Coupon Methodand Portable Consumer Device for Utilizing Same,” the disclosure ofwhich is hereby incorporated herein by reference.

In one embodiment, conditional rewards are provided to the user (101);and the transaction handler (103) monitors the transactions of the user(101) to identify redeemable rewards that have satisfied the respectiveconditions. In one embodiment, the conditional rewards are selectedbased on transaction data (109). Further details and examples of oneembodiment are provided in U.S. patent application Ser. No. 11/862,487,filed Sep. 27, 2007, assigned U.S. Pat. App. Pub. No. 2008/0082418, andentitled “Consumer Specific Conditional Rewards,” the disclosure ofwhich is hereby incorporated herein by reference. The techniques todetect the satisfied conditions of conditional rewards can also be usedto detect the transactions that satisfy the conditions specified tolocate the transactions that result from online activities, such asonline advertisements, searches, etc., to correlate the transactionswith the respective online activities.

Further details about targeted offer delivery in one embodiment areprovided in U.S. patent application Ser. No. 12/185,332, filed Aug. 4,2008, assigned U.S. Pat. App. Pub. No. 2010/0030644, and entitled“Targeted Advertising by Payment Processor History of Cashless AcquiredMerchant Transaction on Issued Consumer Account,” and in U.S. patentapplication Ser. No. 12/849,793, filed Aug. 3, 2010, assigned U.S. Pat.App. Pub. No. 2011/0035280, and entitled “Systems and Methods forTargeted Advertisement Delivery,” the disclosures of which applicationsare hereby incorporated herein by reference.

Profile Matching

In FIG. 1, the user tracker (113) obtains and generates contextinformation about the user (101) at the point of interaction (107),including user data (125) that characterizes and/or identifies the user(101). The profile selector (129) selects a user specific profile (131)from the set of transaction profiles (127) generated by the profilegenerator (121), based on matching the characteristics of thetransaction profiles (127) and the characteristics of the user data(125). For example, the user data (125) indicates a set ofcharacteristics of the user (101); and the profile selector (129)selects the user specific profile (131) that is for a particular user ora group of users and that best matches the set of characteristicsspecified by the user data (125).

In one embodiment, the profile selector (129) receives the transactionprofiles (127) in a batch mode. The profile selector (129) selects theuser specific profile (131) from the batch of transaction profiles (127)based on the user data (125). Alternatively, the profile generator (121)generates the transaction profiles (127) in real time; and the profileselector (129) uses the user data (125) to query the profile generator(121) to generate the user specific profile (131) in real time, or justin time. The profile generator (121) generates the user specific profile(131) that best matches the user data (125).

In one embodiment, the user tracker (113) identifies the user (101)based on the user activity on the transaction terminal (105) (e.g.,having visited a set of websites, currently visiting a type of webpages, search behavior, etc.).

In one embodiment, the user data (125) includes an identifier of theuser (101), such as a global unique identifier (GUID), a personalaccount number (PAN) (e.g., credit card number, debit card number, orother card account number), or other identifiers that uniquely andpersistently identify the user (101) within a set of identifiers of thesame type. Alternatively, the user data (125) may include otheridentifiers, such as an Internet Protocol (IP) address of the user(101), a name or user name of the user (101), or a browser cookie ID,which identify the user (101) in a local, temporary, transient and/oranonymous manner. Some of these identifiers of the user (101) may beprovided by publishers, advertisers, ad networks, search engines,merchants, or the user tracker (113). In one embodiment, suchidentifiers are correlated to the user (101) based on the overlapping orproximity of the time period of their usage to establish anidentification reference table.

In one embodiment, the identification reference table is used toidentify the account information (142) (e.g., account number (302))based on characteristics of the user (101) captured in the user data(125), such as browser cookie ID, IP addresses, and/or timestamps on theusage of the IP addresses. In one embodiment, the identificationreference table is maintained by the operator of the transaction handler(103). Alternatively, the identification reference table is maintainedby an entity other than the operator of the transaction handler (103).

In one embodiment, the user tracker (113) determines certaincharacteristics of the user (101) to describe a type or group of usersof which the user (101) is a member. The transaction profile of thegroup is used as the user specific profile (131). Examples of suchcharacteristics include geographical location or neighborhood, types ofonline activities, specific online activities, or merchant propensity.In one embodiment, the groups are defined based on aggregate information(e.g., by time of day, or household), or segment (e.g., by cluster,propensity, demographics, cluster IDs, and/or factor values). In oneembodiment, the groups are defined in part via one or more socialnetworks. For example, a group may be defined based on social distancesto one or more users on a social network website, interactions betweenusers on a social network website, and/or common data in social networkprofiles of the users in the social network website.

In one embodiment, the user data (125) may match different profiles at adifferent granularity or resolution (e.g., account, user, family,company, neighborhood, etc.), with different degrees of certainty. Theprofile selector (129) and/or the profile generator (121) may determineor select the user specific profile (131) with the finest granularity orresolution with acceptable certainty. Thus, the user specific profile(131) is most specific or closely related to the user (101).

In one embodiment, the advertisement selector (133) uses further data inprioritizing, selecting, generating, customizing and adjusting the userspecific advertisement data (119). For example, the advertisementselector (133) may use search data in combination with the user specificprofile (131) to provide benefits or offers to a user (101) at the pointof interaction (107). For example, the user specific profile (131) canbe used to personalize the advertisement, such as adjusting theplacement of the advertisement relative to other advertisements,adjusting the appearance of the advertisement, etc.

Browser Cookie

In one embodiment, the user data (125) uses browser cookie informationto identify the user (101). The browser cookie information is matched toaccount information (142) or the account number (302) to identify theuser specific profile (131), such as aggregated spending profile (341)to present effective, timely, and relevant marketing information to theuser (101), via the preferred communication channel (e.g., mobilecommunications, web, mail, email, POS, etc.) within a window of timethat could influence the spending behavior of the user (101). Based onthe transaction data (109), the user specific profile (131) can improveaudience targeting for online advertising. Thus, customers will getbetter advertisements and offers presented to them; and the advertiserswill achieve better return-on-investment for their advertisementcampaigns.

In one embodiment, the browser cookie that identifies the user (101) inonline activities, such as web browsing, online searching, and usingsocial networking applications, can be matched to an identifier of theuser (101) in account data (111), such as the account number (302) of afinancial payment card of the user (101) or the account information(142) of the account identification device (141) of the user (101). Inone embodiment, the identifier of the user (101) can be uniquelyidentified via matching IP address, timestamp, cookie ID and/or otheruser data (125) observed by the user tracker (113).

In one embodiment, a look up table is used to map browser cookieinformation (e.g., IP address, timestamp, cookie ID) to the account data(111) that identifies the user (101) in the transaction handler (103).The look up table may be established via correlating overlapping orcommon portions of the user data (125) observed by different entities ordifferent user trackers (113).

In one embodiment, the portal (143) is configured to identify theconsumer account (146) based on the IP address identified in the userdata (125) through mapping the IP address to a street address.

In one embodiment, the portal (143) uses a plurality of methods toidentify consumer accounts (146) based on the user data (125). Theportal (143) combines the results from the different methods todetermine the most likely consumer account (146) for the user data(125).

Details about the identification of consumer account (146) based on userdata (125) in one embodiment are provided in U.S. patent applicationSer. No. 12/849,798, filed Aug. 3, 2010, assigned U.S. Pat. App. Pub.No. 2011/0093327, and entitled “Systems and Methods to MatchIdentifiers,” the disclosure of which is hereby incorporated herein byreference.

Close the Loop

In one embodiment, the correlator (117) is used to “close the loop” forthe tracking of consumer behavior across an on-line activity and an“off-line” activity that results at least in part from the on-lineactivity. In one embodiment, online activities, such as searching, webbrowsing, social networking, and/or consuming online advertisements, arecorrelated with respective transactions to generate the correlationresult (123) in FIG. 1. The respective transactions may occur offline,in “brick and mortar” retail stores, or online but in a context outsidethe online activities, such as a credit card purchase that is performedin a way not visible to a search company that facilitates the searchactivities.

The correlator (117) is configured in one embodiment to identifytransactions resulting from searches or online advertisements. Forexample, in response to a query about the user (101) from the usertracker (113), the correlator (117) identifies an offline transactionperformed by the user (101) and sends the correlation result (123) aboutthe offline transaction to the user tracker (113), which allows the usertracker (113) to combine the information about the offline transactionand the online activities to provide significant marketing advantages.

For example, a marketing department could correlate an advertisingbudget to actual sales. For example, a marketer can use the correlationresult (123) to study the effect of certain prioritization strategies,customization schemes, etc. on the impact on the actual sales. Forexample, the correlation result (123) can be used to adjust orprioritize advertisement placement on a web site, a search engine, asocial networking site, an online marketplace, or the like.

In one embodiment, the profile generator (121) uses the correlationresult (123) to augment the transaction profiles (127) with dataindicating the rate of conversion from searches or advertisements topurchase transactions. In one embodiment, the correlation result (123)is used to generate predictive models to determine what a user (101) islikely to purchase when the user (101) is searching using certainkeywords or when the user (101) is presented with an advertisement oroffer. In one embodiment, the portal (143) is configured to report thecorrelation result (123) to a partner, such as a search engine, apublisher, or a merchant, to allow the partner to use the correlationresult (123) to measure the effectiveness of advertisements and/orsearch result customization, to arrange rewards, etc.

In one embodiment, the correlator (117) matches the online activitiesand the transactions based on matching the user data (125) provided bythe user tracker (113) and the records of the transactions, such astransaction data (109) or transaction records (301). In anotherembodiment, the correlator (117) matches the online activities and thetransactions based on the redemption of offers/benefits provided in theuser specific advertisement data (119).

In one embodiment, the portal (143) is configured to receive a set ofconditions and an identification of the user (101), determine whetherthere is any transaction of the user (101) that satisfies the set ofconditions, and if so, provide indications of the transactions thatsatisfy the conditions and/or certain details about the transactions,which allows the requester to correlate the transactions with certainuser activities, such as searching, web browsing, consumingadvertisements, etc.

In one embodiment, the requester may not know the account number (302)of the user (101); and the portal (143) is to map the identifierprovided in the request to the account number (302) of the user (101) toprovide the requested information. Examples of the identifier beingprovided in the request to identify the user (101) include anidentification of an iFrame of a web page visited by the user (101), abrowser cookie ID, an IP address and the day and time corresponding tothe use of the IP address, etc.

The information provided by the portal (143) can be used in pre-purchasemarketing activities, such as customizing content or offers,prioritizing content or offers, selecting content or offers, etc., basedon the spending pattern of the user (101). The content that iscustomized, prioritized, selected, or recommended may be the searchresults, blog entries, items for sale, etc.

The information provided by the portal (143) can be used inpost-purchase activities. For example, the information can be used tocorrelate an offline purchase with online activities. For example, theinformation can be used to determine purchases made in response to mediaevents, such as television programs, advertisements, news announcements,etc.

Details about profile delivery, online activity to offline purchasetracking, techniques to identify the user specific profile (131) basedon user data (125) (such as IP addresses), and targeted delivery ofadvertisement/offer/benefit in some embodiments are provided in U.S.patent application Ser. No. 12/849,789, filed Aug. 3, 2010, assignedU.S. Pat. App. Pub. No. 2011/0035278, and entitled “Systems and Methodsfor Closing the Loop between Online Activities and Offline Purchases,”the disclosure of which application is incorporated herein by reference.

Loyalty Program

In one embodiment, the transaction handler (103) uses the account data(111) to store information for third party loyalty programs.

FIG. 8 shows the structure of account data (111) for providing loyaltyprograms according to one embodiment. In FIG. 8, data related to a thirdparty loyalty program may include an identifier of the loyalty benefitofferor (183) that is linked to a set of loyalty program rules (185) andloyalty record (187) for the loyalty program activities of the accountidentifier (181). In one embodiment, at least part of the data relatedto the third party loyalty program is stored under the accountidentifier (181) of the user (101), such as the loyalty record (187).

FIG. 8 illustrates the data related to one third party loyalty programof a loyalty benefit offeror (183). In one embodiment, the accountidentifier (181) may be linked to multiple loyalty benefit offerors(e.g., 183), corresponding to different third party loyalty programs.The third party loyalty program of the loyalty benefit offeror (183)provides the user (101), identified by the account identifier (181),with benefits, such as discounts, rewards, incentives, cash back, gifts,coupons, and/or privileges.

In one embodiment, the association between the account identifier (181)and the loyalty benefit offeror (183) in the account data (111)indicates that the user (101) having the account identifier (181) is amember of the loyalty program. Thus, the user (101) may use the accountidentifier (181) to access privileges afforded to the members of theloyalty programs, such as rights to access a member only area, facility,store, product or service, discounts extended only to members, oropportunities to participate in certain events, buy certain items, orreceive certain services reserved for members.

In one embodiment, it is not necessary to make a purchase to use theprivileges. The user (101) may enjoy the privileges based on the statusof being a member of the loyalty program. The user (101) may use theaccount identifier (181) to show the status of being a member of theloyalty program.

For example, the user (101) may provide the account identifier (181)(e.g., the account number of a credit card) to the transaction terminal(105) to initiate an authorization process for a special transactionwhich is designed to check the member status of the user (101), as ifthe account identifier (181) were used to initiate an authorizationprocess for a payment transaction. The special transaction is designedto verify the member status of the user (101) via checking whether theaccount data (111) is associated with the loyalty benefit offeror (183).If the account identifier (181) is associated with the correspondingloyalty benefit offeror (183), the transaction handler (103) provides anapproval indication in the authorization process to indicate that theuser (101) is a member of the loyalty program. The approval indicationcan be used as a form of identification to allow the user (101) toaccess member privileges, such as access to services, products,opportunities, facilities, discounts, permissions, which are reservedfor members.

In one embodiment, when the account identifier (181) is used to identifythe user (101) as a member to access member privileges, the transactionhandler (103) stores information about the access of the correspondingmember privilege in loyalty record (187). The profile generator (121)may use the information accumulated in the loyalty record (187) toenhance transaction profiles (127) and provide the user (101) withpersonalized/targeted advertisements, with or without further offers ofbenefit (e.g., discounts, incentives, rebates, cash back, rewards,etc.).

In one embodiment, the association of the account identifier (181) andthe loyalty benefit offeror (183) also allows the loyalty benefitofferor (183) to access at least a portion of the account data (111)relevant to the loyalty program, such as the loyalty record (187) andcertain information about the user (101), such as name, address, andother demographic data.

In one embodiment, the loyalty program allows the user (101) toaccumulate benefits according to loyalty program rules (185), such asreward points, cash back, levels of discounts, etc. For example, theuser (101) may accumulate reward points for transactions that satisfythe loyalty program rules (185); and the user (101) may use the rewardpoints to redeem cash, gift, discounts, etc. In one embodiment, theloyalty record (187) stores the accumulated benefits; and thetransaction handler (103) updates the loyalty record (187) associatedwith the loyalty benefit offeror (183) and the account identifier (181),when events that satisfy the loyalty program rules occur.

In one embodiment, the accumulated benefits as indicated in the loyaltyrecord (187) can be redeemed when the account identifier (181) is usedto perform a payment transaction, when the payment transaction satisfiesthe loyalty program rules. For example, the user (101) may redeem anumber of points to offset or reduce an amount of the purchase price.

In one embodiment, when the user (101) uses the account identifier (181)to make purchases as a member, the merchant may further provideinformation about the purchases; and the transaction handler (103) canstore the information about the purchases as part of the loyalty record(187). The information about the purchases may identify specific itemsor services purchased by the member. For example, the merchant mayprovide the transaction handler (103) with purchase details atstock-keeping unit (SKU) level, which are then stored as part of theloyalty record (187). The loyalty benefit offeror (183) may use thepurchase details to study the purchase behavior of the user (101); andthe profile generator (121) may use the SKU level purchase details toenhance the transaction profiles (127).

In one embodiment, the SKU level purchase details are requested from themerchants or retailers via authorization responses, when the account(146) of the user (101) is enrolled in a loyalty program that allows thetransaction handler (103) (and/or the issuer processor (145)) to collectthe purchase details.

A method to provide loyalty programs of one embodiment includes the useof the transaction handler (103) as part of a computing apparatus. Thecomputing apparatus processes a plurality of payment card transactions.After the computing apparatus receives a request to track transactionsfor a loyalty program, such as the loyalty program rules (185), thecomputing apparatus stores and updates loyalty program information inresponse to transactions occurring in the loyalty program. The computingapparatus provides to a customer (e.g., 101) an offer of a benefit whenthe customer satisfies a condition defined in the loyalty program, suchas the loyalty program rules (185). In one embodiment, the loyaltybenefit as identified in the loyalty record (187) can be redeemed inconnection with a transaction in a way the benefit of an offer stored inassociation with the account identifier (181) is redeemed.

Examples of loyalty programs through collaboration between collaborativeconstituents in a payment processing system, including the transactionhandler (103) in one embodiment are provided in U.S. patent applicationSer. No. 11/767,202, filed Jun. 22, 2007, assigned U.S. Pat. App. Pub.No. 2008/0059302, and entitled “Loyalty Program Service,” U.S. patentapplication Ser. No. 11/848,112, filed Aug. 30, 2007, assigned U.S. Pat.App. Pub. No. 2008/0059306, and entitled “Loyalty Program IncentiveDetermination,” and U.S. patent application Ser. No. 11/848,179, filedAug. 30, 2007, assigned U.S. Pat. App. Pub. No. 2008/0059307, andentitled “Loyalty Program Parameter Collaboration,” the disclosures ofwhich applications are hereby incorporated herein by reference.

Examples of processing the redemption of accumulated loyalty benefitsvia the transaction handler (103) in one embodiment are provided in U.S.patent application Ser. No. 11/835,100, filed Aug. 7, 2007, assignedU.S. Pat. App. Pub. No. 2008/0059303, and entitled “TransactionEvaluation for Providing Rewards,” the disclosure of which is herebyincorporated herein by reference.

In one embodiment, the incentive, reward, or benefit provided in theloyalty program is based on the presence of correlated relatedtransactions. For example, in one embodiment, an incentive is providedif a financial payment card is used in a reservation system to make areservation and the financial payment card is subsequently used to payfor the reserved good or service. Further details and examples of oneembodiment are provided in U.S. patent application Ser. No. 11/945,907,filed Nov. 27, 2007, assigned U.S. Pat. App. Pub. No. 2008/0071587, andentitled “Incentive Wireless Communication Reservation,” the disclosureof which is hereby incorporated herein by reference.

In one embodiment, the transaction handler (103) provides centralizedloyalty program management, reporting and membership services. In oneembodiment, membership data is downloaded from the transaction handler(103) to acceptance point devices, such as the transaction terminal(105). In one embodiment, loyalty transactions are reported from theacceptance point devices to the transaction handler (103); and the dataindicating the loyalty points, rewards, benefits, etc. are stored on theaccount identification device (141). Further details and examples of oneembodiment are provided in U.S. patent application Ser. No. 10/401,504,filed Mar. 27, 2003, assigned U.S. Pat. App. Pub. No. 2004/0054581, andentitled “Network Centric Loyalty System,” the disclosure of which ishereby incorporated herein by reference.

In one embodiment, the portal (143) of the transaction handler (103) isused to manage reward or loyalty programs for entities such as issuers,merchants, etc. The cardholders, such as the user (101), are rewardedwith offers/benefits from merchants. The portal (143) and/or thetransaction handler (103) track the transaction records for themerchants for the reward or loyalty programs. Further details andexamples of one embodiment are provided in U.S. patent application Ser.No. 11/688,423, filed Mar. 20, 2007, assigned U.S. Pat. App. Pub. No.2008/0195473, and entitled “Reward Program Manager,” the disclosure ofwhich is hereby incorporated herein by reference.

In one embodiment, a loyalty program includes multiple entitiesproviding access to detailed transaction data, which allows theflexibility for the customization of the loyalty program. For example,issuers or merchants may sponsor the loyalty program to provide rewards;and the portal (143) and/or the transaction handler (103) stores theloyalty currency in the data warehouse (149). Further details andexamples of one embodiment are provided in U.S. patent application Ser.No. 12/177,530, filed Jul. 22, 2008, assigned U.S. Pat. App. Pub. No.2009/0030793, and entitled “Multi-Vender Multi-Loyalty CurrencyProgram,” the disclosure of which is hereby incorporated herein byreference.

In one embodiment, an incentive program is created on the portal (143)of the transaction handler (103). The portal (143) collects offers froma plurality of merchants and stores the offers in the data warehouse(149). The offers may have associated criteria for their distributions.The portal (143) and/or the transaction handler (103) may recommendoffers based on the transaction data (109). In one embodiment, thetransaction handler (103) automatically applies the benefits of theoffers during the processing of the transactions when the transactionssatisfy the conditions associated with the offers. In one embodiment,the transaction handler (103) communicates with transaction terminals(105) to set up, customize, and/or update offers based on market focus,product categories, service categories, targeted consumer demographics,etc. Further details and examples of one embodiment are provided in U.S.patent application Ser. No. 12/413,097, filed Mar. 27, 2009, assignedU.S. Pat. App. Pub. No. 2010/0049620, and entitled “Merchant DeviceSupport of an Integrated Offer Network,” the disclosure of which ishereby incorporated herein by reference.

In one embodiment, the transaction handler (103) is configured toprovide offers from merchants to the user (101) via the payment system,making accessing and redeeming the offers convenient for the user (101).The offers may be triggered by and/or tailored to a previoustransaction, and may be valid only for a limited period of time startingfrom the date of the previous transaction. If the transaction handler(103) determines that a subsequent transaction processed by thetransaction handler (103) meets the conditions for the redemption of anoffer, the transaction handler (103) may credit the consumer account(146) for the redemption of the offer and/or provide a notificationmessage to the user (101). Further details and examples of oneembodiment are provided in U.S. patent application Ser. No. 12/566,350,filed Sep. 24, 2009, assigned U.S. Pat. App. Pub. No. 2010/0114686, andentitled “Real-Time Statement Credits and Notifications,” the disclosureof which is hereby incorporated herein by reference.

Details on loyalty programs in one embodiment are provided in U.S.patent application Ser. No. 12/896,632, filed Oct. 1, 2010, assignedU.S. Pat. App. Pub. No. 2011/0087530, and entitled “Systems and Methodsto Provide Loyalty Programs,” the disclosure of which is herebyincorporated herein by reference.

SKU

In one embodiment, merchants generate stock-keeping unit (SKU) or otherspecific information that identifies the particular goods and servicespurchased by the user (101) or customer. The SKU information may beprovided to the operator of the transaction handler (103) that processedthe purchases. The operator of the transaction handler (103) may storethe SKU information as part of transaction data (109), and reflect theSKU information for a particular transaction in a transaction profile(127 or 131) associated with the person involved in the transaction.

When a user (101) shops at a traditional retail store or browses awebsite of an online merchant, an SKU-level profile associatedspecifically with the user (101) may be provided to select anadvertisement appropriately targeted to the user (101) (e.g., via mobilephones, POS terminals, web browsers, etc.). The SKU-level profile forthe user (101) may include an identification of the goods and serviceshistorically purchased by the user (101). In addition, the SKU-levelprofile for the user (101) may identify goods and services that the user(101) may purchase in the future. The identification may be based onhistorical purchases reflected in SKU-level profiles of otherindividuals or groups that are determined to be similar to the user(101). Accordingly, the return on investment for advertisers andmerchants can be greatly improved.

In one embodiment, the user specific profile (131) is an aggregatedspending profile (341) that is generated using the SKU-levelinformation. For example, in one embodiment, the factor values (344)correspond to factor definitions (331) that are generated based onaggregating spending in different categories of products and/orservices. A typical merchant offers products and/or services in manydifferent categories.

In one embodiment, the SKU level purchase details are requested from themerchants or retailers via authorization responses, when the account(146) of the user (101) is enrolled in a program that allows thetransaction handler (103) (and/or the issuer processor (145)) to collectthe purchase details. Based on the SKU information and perhaps othertransaction data, the profile generator (121) may create an SKU-leveltransaction profile for the user (101). In one embodiment, based on theSKU information associated with the transactions for each personentering into transactions with the operator of the transaction handler(103), the profile generator (121) may create an SKU-level transactionprofile for each person.

Details on SKU-level profile in one embodiment are provided in U.S.patent application Ser. No. 12/899,144, filed Oct. 6, 2010, assignedU.S. Pat. App. Pub. No. 2011/0093335, and entitled “Systems and Methodsfor Advertising Services Based on an SKU-Level Profile,” the disclosureof which is hereby incorporated herein by reference.

Real-Time Messages

In one embodiment, the transaction handler (103) is configured tocooperate with the media controller (115) to facilitate real-timeinteraction with the user (101) when the payment of the user (101) isbeing processed by the transaction handler (103). The real-timeinteraction provides the opportunity to impact the user experienceduring the purchase (e.g., at the time of card swipe), throughdelivering messages in real-time to a point of interaction (107), suchas a mobile phone, a personal digital assistant, a portable computer,etc. The real-time message can be delivered via short message service(SMS), email, instant messaging, or other communications protocols.

In one embodiment, the real-time message is provided without requiringmodifications to existing systems used by the merchants and/or issuers.

FIG. 9 shows a system to provide real-time messages according to oneembodiment. In FIG. 9, the transaction handler (103) (or a separatecomputing system coupled with the transaction handler (103)) is todetect the occurrence of certain transactions of interest during theprocessing of the authorization requests received from the transactionterminal (105); a message broker (201) is to identify a relevant messagefor the user (101) associated with the corresponding authorizationrequest; and the media controller (115) is to provide the message to theuser (101) at the point of interaction (107) via a communication channelseparate from the channel used by the transaction handler (103) torespond to the corresponding authorization request submitted from thetransaction terminal (105).

In one embodiment, the media controller (115) is to provide the messageto the point of interaction (107) in parallel with the transactionhandler (103) providing the response to the authorization request.

In one embodiment, the point of interaction (107) receives the messagefrom the media controller (115) in real-time with the transactionhandler (103) processing the authorization request. In one embodiment,the message is to arrive at the point of interaction (107) in thecontext of the response provided from the transaction handler (103) tothe transaction terminal (105). For example, the message is to arrive atthe point of interaction (107) substantially at the same time as theresponse to the authorization request arrives at the transactionterminal, or with a delay not long enough to cause the user (101) tohave the impression that the message is in response to an action otherthat the payment transaction. For example, the message is to arrive atthe point of interaction (107) prior to the user (101) completing thetransaction and leaving the transaction terminal (105), or prior to theuser (101) leaving the retail location of the merchant operating thetransaction terminal (105).

In FIG. 9, the system includes a portal (143) to provide services tomerchants and/or the user (101).

For example, in one embodiment, the portal (143) allows the user (101)to register the communication reference (205) in association with theaccount data (111), such as the account information (142) of theconsumer account (146); and the media controller (115) is to use thecommunication reference (205) to deliver the message to the point ofinteraction (107). Examples of the communication reference (205)includes a mobile phone number, an email address, a user identifier ofan instant messaging system, an IP address, etc.

In one embodiment, the portal (143) allows merchants and/or otherparties to define rules (203) to provide offers (186) as real-timeresponses to authorization requests; and based on the offer rules (203),the message broker (201) is to generate, or instruct the mediacontroller to generate, the real-time message to provide the offers(186) to the user (101). For example, the offer (186) may include adiscount, an incentive, a reward, a rebate, a gift, or other benefit,which can be redeemed upon the satisfaction of certain conditionsrequired by the offer rules (203). In one embodiment, based on the offerrules (203) the message broker (201) configures a message by selectingthe appropriate message template from (an) existing message(s)template(s), and inserts any relevant data (e.g., the communicationreference (205)) into the selected template, then passes the configuredmessage to the media controller (115), which delivers the message to thepoint of interaction (107). In one embodiment, the message broker (201)(or a subsystem) is used to manage message templates along with therules for selecting the appropriate message template from among severalpotential choices.

In one embodiment, the offer rules (203) include offer details,targeting rules, advertisement campaign details, profile mapping,creative mapping, qualification rules, award/notify/fulfillment rules,approvals, etc. Creative elements for offers include text, images,channels, approvals, etc.

In one embodiment, when the offer rules (203) are activated by themerchant or advertiser via the portal (143), the message broker (201) isto generate trigger records (207) for the transaction handler (103). Thetransaction handler (103) is to monitor the incoming authorizationrequests to identify requests that satisfy the conditions specified inthe trigger records (207) during the process of the authorizationrequests, and to provide the information about the identified requeststo the message broker (201) for the transmission of an appropriatereal-time message in accordance with the offer rules (203).

In one embodiment, the generation of the trigger records (207) for thetransaction handler (103) is in real-time with the merchant oradvertiser activating the offer rules (203). Thus, the offer rules (203)can be activated and used for the detection of the new authorizationrequests in real-time, while the transaction handler (103) continues toprocess the incoming authorization requests.

In one embodiment, the portal (143) provides information about thespending behaviors reflected in the transaction data (109) to assist themerchants or advertisers to target offers or advertisements. Forexample, in one embodiment, the portal (143) allows merchants to targetthe offers (186) based on transaction profiles (127). For example, theoffer rules (203) are partially based on the values in a transactionprofile (127), such as an aggregated spending profile (341). In oneembodiment, the offer rules (203) are partially based on the informationabout the last purchase of the user (101) from the merchant operatingthe transaction terminal (105) (or another merchant), and/or theinformation about the location of the user (101), such as the locationdetermined based on the location of the transaction terminal (105)and/or the location of the merchant operating the transaction terminal(105).

In one embodiment, the portal (143) provides transaction basedstatistics, such as merchant benchmarking statistics, industry/marketsegmentation, etc., to assist merchants and advertisers to identifycustomers.

Thus, the real-time messages can be used to influence customer behaviorswhile the customers are in the purchase mode.

In one embodiment, the benefit of the offers (186) can be redeemed viathe transaction handler (103). The redemption of the offer (186) may ormay not require the purchase details (e.g., SKU level purchase details).Details in one embodiment about redeeming offers (186) via thetransaction handler (103) are provided in U.S. patent application Ser.No. 13/113,710, filed May 23, 2011, assigned U.S. Pat. App. Pub. No.2011/0288918, and entitled “Systems and Methods for Redemption ofOffers,” the disclosure of which is hereby incorporated herein byreference.

In one embodiment, when the authorization request for a purchaseindicates that the purchase qualifies the offer (186) for redemption ifthe purchase corresponding to the authorization request is completed,the message broker (201) is to construct a message and use the mediacontroller (115) to deliver the message in real-time with the processingof the authorization request to the point of interaction (107). Themessage informs the user (101) that when the purchase is completed, thetransaction handler (103) and/or the issuer processor (145) is toprovide the benefit of the offer (186) to the user (101) via statementcredit or some other settlement value, for example points in aregistered loyalty program, or credit at the point of sale using adigital coupon delivered to the purchaser via cell phone.

In one embodiment, the settlement of the payment transactioncorresponding to the authorization request does not occur in real-timewith the processing of the authorization request. For example, themerchant may submit the complete purchases for settlement at the end ofthe day, or in accordance with a predetermined schedule. The settlementmay occur one or more days after the processing of the authorizationrequest.

In one embodiment, when transactions are settled, the settledtransactions are matched to the authorization requests to identifyoffers (186) that are redeemable in view of the settlement. When theoffer (186) is confirmed to be redeemable based on a record ofsuccessful settlement, the message broker (201) is to use the mediacontroller (115) to provide a message to the point of interaction (107)of the user (101), such as the mobile phone of the user (101). In oneembodiment, the message is to inform the user (101) of the benefit to beprovided as statement credits and/or to provide additional offers. Inone embodiment, the message to confirm the statement credits istransmitted in real-time with the completion of the transactionsettlement.

In one embodiment, the message broker (201) is to determine the identityof the merchant based on the information included in the authorizationrequest transmitted from the transaction terminal (105) to thetransaction handler (103). In one embodiment, the identity of themerchant is normalized to allow the application of the offer rules (203)that are merchant specific.

In one embodiment, the portal (143) is to provide data insight tomerchants and/or advertisers. For example, the portal (143) can providethe transaction profile (127) of the user (101), audience segmentationinformation, etc.

In one embodiment, the portal (143) is to allow the merchants and/oradvertisers to define and manage offers for their creation, fulfillmentand/or delivery in messages.

In one embodiment, the portal (143) allows the merchants and/oradvertisers to test, run and/or monitor the offers (186) for theircreation, fulfillment and/or delivery in messages.

In one embodiment, the portal (143) is to provide reports and analyticsregarding the offers (186).

In one embodiment, the portal (143) provides operation facilities, suchas onboarding, contact management, certification, file management,workflow, etc. to assist the merchants and/or advertisers to completethe tasks related to the offers (186).

In one embodiment, the portal (143) allows the user (101) to opt in oropt out of the real-time message delivery service.

In one embodiment, an advertiser or merchant can select an offerfulfillment method from a list of options, such as statement credits,points, gift cards, e-certificates, third party fulfillment, etc.

In one embodiment, the merchant or advertiser is to use the “off therack” transaction profiles (127) available in the data warehouse (149).In one embodiment, the merchant or advertiser can further editparameters to customize the generation of the transaction profiles (127)and/or develop custom transaction profiles from scratch using the portal(143).

In one embodiment, the portal (143) provides a visualization tool toallow the user to see clusters of data based on GeoCodes, proximity,transaction volumes, spending patterns, zip codes, customers, stores,etc.

In one embodiment, the portal (143) allows the merchant or advertiser todefine cells for targeting the customers in the cells based ondate/time, profile attributes, map to offer/channel/creative, conditiontesting, etc.

In one embodiment, the portal (143) allows the merchant or advertiser tomonitor the system health, such as the condition of servers, filesreceived or sent, errors, status, etc., the throughput by date or range,by program, by campaign, or by global view, and aspects of currentprograms/offers/campaigns, such as offer details, package audit reports,etc. In one embodiment, reporting includes analytics and metrics, suchas lift, conversion, category differentials (e.g., spending patterns,transaction volumes, peer groups), and reporting by program, campaign,cell, GeoCode, proximity, ad-hoc, auditing, etc.

FIG. 10 shows a method to provide real-time messages according to oneembodiment. In FIG. 10, a computing apparatus is to generate (211) atrigger record (207) for a transaction handler (103) to identify anauthorization request that satisfies the conditions specified in thetrigger record (207), receive (213) from the transaction handler (103)information about the authorization request in real-time with thetransaction handler (103) providing a response to the authorizationrequest to a transaction terminal (105), identify (215) a communicationreference (205) of a user (101) associated with the authorizationrequest, determine (217) a message for the user (101) responsive to theauthorization request, and provide (219) the message to the user (101)at a point of interaction (107) via the communication reference (205),in parallel with the response from the transaction handler (103) to thetransaction terminal (105).

In one embodiment, the computing apparatus includes at least one of: atransaction handler, a message broker (201), a media controller (115), aportal (143) and a data warehouse.

Selection of Locations from Route Points or Route Paths

FIG. 11 shows a schematic diagram wherein a location provider (140) thatinteracts with a route provider (1102) associated with a user (101). Inan embodiment the likely route that will be traversed by the user (101)can be predicted by a route predictor (1150) associated with thelocation provider (140). The route predictor (1150) can obtaininformation from the route provider (1102) such as an origin/start point(1108) or portion of a route traversed by the user (101) to predict thelikely route of the user (101). In an embodiment, the route predictor(1150) can consult a route dictionary (not shown) associated with theuser (101) in order to make the route prediction. For example, the routepredictor (1150) can predict that the route (1106) will likely be takenby the user (101) based on the start point or origin (1108) and theinformation that the user (101) traversed the first two segments (1110)and (1112) of the route (1106).

In an embodiment, the location provider (140) obtains the informationregarding a route selected (1106) for traversal by the user (101) fromthe route provider (1102). In an embodiment, the route provider (1102)can comprise any equipment that provides GPS data of the user, such as,a GPS device or a smartphone or a device which includes an IP addressthat may be mapped to the user's location. Accordingly, a route selected(1106) for traversal by the user (101) is obtained by the locationprovider (140) which in conjunction with data from the user specificprofile (131) can predict the likely locations that are of interest(144) to the user (101) along the selected route (1106) and within apredetermined distance from the selected route (1106). In an embodiment,the locations of interest (144) can comprise retail outlets,entertainment establishments or other locations where the user (101) islikely to spend time. Consequently, real time messages associated withsuch locations of interest (144) can be provided to the user by theportal (143) in accordance with embodiments that will be described infurther detail herein. This facilitates the portal (143) to providelocation-based services such as, real-time messages related to deals,coupons or other alerts to the user (101) that are tailored to theuser's (101) interests.

FIG. 12a shows a schematic diagram of a route predictor (1150) inaccordance with an embodiment. The route predictor (1150) is configuredto predict a route (1106) that a user (101) will likely employ andcommunicate the predicted route (1106) to the portal (143). In anembodiment, the route predictor (1150) includes a location informationreceiver (1242), a route dictionary builder (1244), a route predictiongenerator (1246) and a location information transmitter (1248). Thelocation information receiver (1242) is configured to receive locationinformation of consumers associated with the consumer accounts. In anembodiment, the location information receiver (1242) can receive the GPSinformation or other network information, indicative of location of theconsumers. In one embodiment, a consumer or a user (101) can besolicited to provide access to their current location information inorder to receive location-based services. For example, when a user (101)consents to collection of his or her current location information, theuser (101) can be requested to provide their mobile number or otherinformation that can aid collection of current location information.This enables obtaining current location information from the consumer'smobile phone and location-based services appropriate to the consumer'slocation and context can be provided. In an embodiment, the locationinformation receiver (1242) can receive location information of a user'sstarting location (1108) and/or an initial portion (1110+1112) of theroute traversed by the user (101) to facilitate generating the routeprediction (1252).

The current location information collected by the location informationreceiver (1242) is provided to a route dictionary builder (1244) thatbuilds and updates a database or a dictionary (1150) of a user's routeswherein each route is coded into a collection of identifiers such as,letters which form a term or a word in the route dictionary (1150). Inan embodiment, the contents of the route dictionary (1152) are accessedby the route prediction generator (1246) to predict a route (1106) thatis likely to be travelled by the user (101). This is facilitated byemploying language processing techniques to analyze contents of theroute dictionary (1152) for the route prediction (1106). In anembodiment, context data which can also be recorded in the routedictionary (1152) can be employed for the route prediction (1106). In anembodiment, context data from external sources (not shown) inconjunction with the route dictionary (1152) contents can also beemployed for making route prediction. The route selected by the routeprediction generator (1246) is provided to a route informationtransmitter (1248) which transmits the route prediction (1106) to arequestor. In an embodiment, the requestor can be the portal (143) whichis configured to provide location-based services. In an embodiment, therequestor can be a third-party requestor, such as, a merchant or anotherintermediate entity that logs request for the route prediction.

FIG. 12b shows a schematic diagram of a location provider (140) inaccordance with an embodiment. The location provider (140) is configuredto determine locations (144) along the route (1106) that will likely beof interest to the user (101) and provide information related to thelocations of interest (144) to the portal (143). In an embodiment, thelocation provider (140) comprises a route information receiver (1272), aroute analyzer (1274), a location predictor (1276) and a locationinformation transmitter (1278). The route location information receiver(1242) is configured to receive selected route information of consumersassociated with the consumer accounts. In an embodiment, the routeinformation receiver (1272) can receive the GPS information or othernetwork information, indicative of current location and a selected routeof the consumers. In one embodiment, a consumer or a user (101) can besolicited to provide access to their current location information inorder to receive location-based services. For example, when a user (101)consents to collection of his or her current location information, theuser (101) can be requested to provide their mobile number or otherinformation that can aid collection of the current location information.This enables obtaining the current location and a selected route (1106)or predicting that the route (1106) will likely be selected from dataprovided by the consumer's or user's (101) mobile phone andlocation-based services appropriate to the user's location (101) andcontext can be provided. In an embodiment, the route informationreceiver (1272) can receive selected route information (1106) of theuser (101) to facilitate determining the locations of interest (144) tothe user (101) along the selected route (1106).

The selected route information (1106) collected by the route informationreceiver (1272) is provided to a route analyzer (1274) which analyzesthe selected route in accordance with embodiments described herein. Theresults of the selected route analysis are transmitted to a locations ofinterest predictor (1276) which is also comprised in the locationprovider (140). The locations of interest predictor (1276) canadditionally receive profile information (131) of the user (101) fromthe portal (143) and generates predictions or determines locations ofinterest (144) to the user (101) along the user's (101) selected route(1106).

In an embodiment, the profile information (131) of the user (101)comprises the affinity values (1250) of the user (101) for variousmerchants, vendors, products and/or locations. In this embodiment, acomputing apparatus can perform cluster analysis in accordance withtechniques know in the art to identify a plurality of standardizedclusters corresponding to an area of products or services based ontransactions processed by the transaction handler. In an embodiment,each of the plurality of standardized clusters corresponds to a clusterof account holders, such as user (101), that have similar spendingpatterns. Thus, a set of affinity values (1250) can be computed for theuser (101) based on transaction data of the user (101) to indicatecloseness or predilection of the user (101) to the set of standardizedclusters. For example, if the user (101) buys a lot of electronics, theaffinity value of the consumer for the electronics cluster and for acluster of other users who have similar buying patterns is high. By theway of illustration and not limitation, the affinity values can berepresented in as a range from 0 (representing least affinity) to 1(representing high affinity) in accordance with an embodiment.

In an embodiment, the locations of interest (144) can be determinedbased not only on their distances from the route (1106) but also basedon the user affinity values (1250). In accordance with embodimentsdescribed further herein, the distances of locations along the route(1106) can be further weighed with the user affinity values (1250) toderive particular utility values associated with each of the locationsof interest (144), which utility values can be employed to furtherfilter or grade the locations of interest (144) and thereby promoteparticular offers via real-time messaging to the user (101).

The locations information transmitter (1278) receives the informationregarding the locations that are likely to be of interest to the user(101) along the selected route from the locations of interest predictor(1276) and transmits the received information to the portal (143) to aidthe portal (143) in providing appropriate location based services to theuser (101).

FIG. 13 shows a method of predicting a route and updating the routedictionary (1152) by a computing apparatus according to one embodiment.Initially, a portion of the route traversed by the user (101) isreceived (1302). A route dictionary (1152) associated with the user isaccessed (1304) and a partial term that encodes the portion of the routetraversed by the user is retrieved (1306). The user's (101) likely route(1106) is predicted (1308) based at least on the partial term whichindicates a portion of the route already traversed by the user (101). Ina further embodiment the route dictionary (1152) can be updated byobserving and recording user behavior. Thus, the computing apparatusthat is configured to provide route prediction (1106) can be furtherconfigured to update the route dictionary (1152) by monitoring theactual route traversed by the user (101) and recording the accuracy ofthe route prediction. In one embodiment, the route traversed by the user(101) is monitored (1310) by the computing apparatus. In an embodiment,the computing apparatus updates the route dictionary (1152) with theroute taken by the user (101) at a later time according to the proceduredetailed herein. The computing apparatus determines (1312) if the user(101) employed the predicted route (1106). If it is determined that theuser (101) employed the predicted route (1106), the count of thepredicted route (1106) is updated (1314). If it is determined that theuser (101) did not employ the predicted route (1106), then adetermination is further made (1316) if the user (101) traversed a routealready recorded or currently existing in the route dictionary (1152).If the route traversed by the user already exists in the routedictionary (1152), the count of the existing route is updated (1320). Ifthe route taken by the user (101) does not exist in the route dictionary(1152), the new route information is recorded (1318) in the routedictionary (1152) with the frequency count set to 1 (1322).Subsequently, as the user (101) employs the newly recorded route, itsfrequency count in the route dictionary (1152) can be updatedaccordingly. In an embodiment, if a route is not used for longer than apreset threshold time, the route can be deleted from the routedictionary (1152) thereby optimizing usage of resources and the routeprediction process. The computing apparatus can therefore be configuredto not only predict routes but also to observe the accuracy of routepredictions and learn from them so that the accuracy of the routepredictions can be increased with increasing observations regarding userbehavior.

FIG. 14a is a map 1300 showing various routes to frequent destinationsof a user (101) who employs a routing device or a route provider (1102)wherein a route (1106) is selected for traversal by the user (101) or ispredicted as the likely route of the user (101). The informationregarding the route (1106) is obtained by the location provider (140)either directly from the route provider (1102) or via the portal (143).The selected route (1106) is analyzed by the route analyzer (1244) andeach intersection of two routes or points at which the user (101)changes direction of travel while travelling along the selected route(1106) is encoded as a vertex by a computing apparatus executing thelocation provider (140). Therefore, five vertices, v1 below the startpoint, v2 next to the convenience store, v3 next to the gas station, v4at the intersection of the route from the gas station to the routeleading to the end point at the factory and v5 near the end point areidentified by the route analyzer (1244) along the route (1106). Inaddition, six locations ls, l1, l2, l3, l4, l5 and le along the route(1106) including the starting location or origin ls and ending locationor destination le are identified by the route analyzer (1244) whichfurther analyzes the route (1106) in accordance with embodimentsdetailed further herein.

FIG. 14b is an illustration showing further analysis of the route (1106)by the route analyzer (1244). It may be appreciated that while the mapsillustrated in the drawings and described herein can be drawn oranalyzed to scale, which scale may be different for different routesbased on, for example, the parameters of the route (1106) such as lengthof the route, number of locations on the route and the like. Inparticular, FIG. 14b illustrates the concept of radii of map verticeswherein five circles are drawn with the vertices v1, v2, v3, v4 and v5as centers and respective radii r1, r2, r3, r4 and r5. Each radius isdetermined so that each of the circles at one of the vertices will haveat least one point of contact with the adjacent circles drawn around theneighboring vertices. For example, the circle drawn at vertex v4 andradius r5 has one point of contact with a neighboring circle drawn at v3and two points of contact with the circle drawn at v5.

FIG. 15 is another illustration showing further analysis of the route(1106) by the route analyzer (1244) wherein the segment distancesbetween route vertices are measured. The distance rs1 between thestarting point and vertex v1 is 17/16 units, the distance rs2 betweenthe vertex v1 and vertex v2 is 23/8 units, the distance rs3 between thevertex v2 and the vertex v3 is 41/2 units, the distance rs4 between thevertex v3 and vertex v4 is 21/4 units, the distance rs5 between thevertex v4 and vertex v5 is 21/8 units, and the distance rse between thevertex v5 and the vertex at the end is 7/8 units.

FIG. 16 shows a map wherein radii of map vertices are weighed to ensurethat the entire area that falls within 1 inch of the route (1106) iscovered for further route analysis. In particular, FIG. 16 shows thecalculation of weighed radius for a segment of the route (1106) betweenthe vertices v2 and v3 is selected as it is the longest segment having alength of 41/4 units. The weighed radius for the selected segmentbetween vertices v2 and v3 is 2.4622 units. By the way of illustrationand not limitation, a weighted radius wr for two vertices vx and vy iscalculated using the formula:

wrvxvy=[(rsxy/2)2+rt2]1/2  Eq. (1)

The weighted radius wrvxvy in accordance with this embodiment is thehypotenuse of a right triangle with sides a=lrs3/2 and b=route threshold(which is 1 inch for the values shown in FIG. 15).

FIG. 17 illustrates the values of the weighted radius for each of thesegments between the vertices of the route (1106) calculated inaccordance with an embodiment of the present disclosure. In anembodiment, it is estimated using Eq. (1) discussed above. The value ofthe weighted radius wrv0v1 for the route segment between the startingpoint and vertex v1 is 1.3703 units, the weighted radius wrv1v2 for theroute segment between the vertices v1 and v2 is 1.5525 units, the valueof the weighted radius wrv2v3 for the route segment between the vertexv2 and the vertex v3 is 2.4622 units, the value of the weighted radiuswrv3v4 between the vertex v3 and vertex v4 is 1.5052 units, the value ofthe weighted radius wrv5ve between vertex v5 and the vertex at the endis 1.0915 units.

FIG. 18 illustrates further analysis of the route (1106) wherein twocircles are drawn for each of the route vertices v1, v2, v3, v4, and v5.Each of the two circles centered at a route vertex has one of the twoweighted radii as a radius of the circle. Thus, for example, for thevertex v1 a first circle is drawn with v1 as the center with a radius ofwrv0v1 equal to 1.3703 units and a second circle can be drawn with v1 asthe center with a radius of wrv1 v2 equal to 1.5525 units. For vertex v2a first circle is drawn with v2 as the center with a radius of wrv1 v2and a second circle can be drawn with v2 as the center with a radius ofwrv2v3. For vertex v3 a first circle is drawn with v3 as the center witha radius of wrv2v3 and a second circle can be drawn with v2 as thecenter with a radius of wrv3v4. For vertex v4 a first circle is drawnwith v4 as the center with a radius of wrv3v4 and a second circle can bedrawn with v4 as the center with a radius of wrv4v5. For vertex v5 afirst circle is drawn with v5 as the center with a radius of wrv4v5 anda second circle can be drawn with v5 as the center with a radius ofwrv5ve. It may be noted that for the vertices vs and ve locatedrespectively at the starting and the ending locations, only one circleis obtained since only one weighted radius is associated with each ofthese two vertices.

FIG. 19 illustrates further analysis of the route (1106) wherein areasurrounding the route (1106) based on the maximum weighted radii fromthe vertices is obtained. Thus, for each of the vertices v1, v2, v3, v4,and v5 having two circles associated with it, the circle with the largerradius is selected for further analysis. This can reduce the number ofcircles involved in the analysis while covering the same area on the mapas shown in FIG. 18.

FIG. 20 illustrates the area around the route (1302) that is selectedfor further analysis based on the contiguous vertices method inaccordance with an embodiment. The area along the route (1106) less thana predetermined distance, for example, 1 inch from the route (1106) iscovered by the outline 2002 and as seen at 2004, the area next to thegas station is the point that is closest to the route (1106).

FIG. 21 illustrates the area around the route (1302) that is selectedfor further analysis to identify locations of interest (144) based onweighted radii from mid points. The outline 2102 that shows the selectedarea 2104 to identify locations of interest (144). The outline 2102 isobtained by drawing a circle at each mid-point of each of the segmentsof the route (1106) extending between vertices vs, v1, v2, v3, v4, v5and ve with radius equal to, for example, distance between the mid-pointof a given route segment and one of the vertices of the given routesegment (or half of the total length of the route segment). Thus, theentire area of the map 1300 within a predetermined distance limit, forexample, 1 inch of the route (1106) is covered.

FIG. 22 illustrates an example of another selected route (2202) which ischaracterized by irregular angles in contrast to the route (1106) thatonly comprises right angles or straight lines which can be special casesand are generally not a real-world scenario. The selected route (2202)comprises three vertices v1, v2, and v3 wherein the route segmentsintersect at irregular angles or angles other than 90 degrees or 180degrees. For example the angle A at vertex v1 is approximately 135degrees. The segment distance between route vertices v0 and v1 measuredaccording to embodiments described herein is rsv0v1=41/2 units andassociated weighted radius wrv0v1 calculated according to embodimentsdescribed herein is 2.4622 units. Similarly, the segment distancebetween route vertices v1 and v2 measured according to embodimentsdescribed herein is rsv1v2=31/4 units and associated weighted radiuswrv1v2 calculated according to embodiments described herein is 1.908units.

FIG. 23 illustrates a scenario wherein using weighted radii frommid-points of route segments when analyzing the selected route (2202)can result in a loss of accuracy for oblique angles or angles not equalto 180 degrees. For example, as seen at vertex v1 the map area aroundthe selected route (2200) is asymmetrically covered so that more than 1inch (route threshold) is covered on the left side while less than 1inch is covered on the right side of the selected route (2200).

FIG. 24 illustrates an embodiment of a “joint radius” method that can beemployed to correct the aforementioned loss of map area coverage on oneside of the selected route (2202) due to the occurrence of an obliqueangle at the vertex v1. In this embodiment, the weighted radii for midpoints of route segments that intersect at oblique angles are furtherbiased or weighed more heavily to one side of the route (2202) or vertexv1 versus the other side of the selected route (2202) or vertex v1 basedon the angle at the vertex v1 that faces each particular side of theroute (2202). Alternately, the weighted radius wrvxvy can be shiftedfrom one side of the selected route (2202) to the other side of theselected route (2202).

FIG. 25a shows one embodiment of a method to adjust the size of theradius in order to cover the map area around the selected route (1106)within a radius equal to a route threshold (rt) which, in accordancewith one embodiment, can be one inch. The method involves reducing thearea to be searched for locations when it is determined that the segmentsize is greater than the route threshold (rt) which, in this embodiment,is one inch. Therefore, the route analyzer (1244) can have a defaultmaximum value set for the length of the route segments as equal to theroute threshold value (rt). Therefore, those route segments havinglengths greater than one inch are divided into sub-segments.

Accordingly, route segment between the starting point and the vertex v1is divided into sub-segments rs1A and rs1B. Similarly the route segmentbetween vertices v1 and v2 is divided into rs2A and rs2B in addition toincluding part of the sub-segment rs2c, the route segment betweenvertices v2 and v3 comprises the remainder of the sub-segment rs2c, rs3Aand rs3B. Similarly other segments between the vertices v3 and v4, v4and v5, v5 and the ending location or the destination are respectivelydivided into sub-segments rs3C, rs3D, rs3E, rs4A, rs4B, rs4C, rs5A,rs5B, rs5C and rs6, as shown in FIG. 25 a.

FIG. 25b shows one embodiment of a method to reduce search size bycreating sub-segments and “sub-vertices” at each sub-segment. As shownin FIG. 25b the end points of each sub-segment that do not coincide withone of the route vertices are identified as “sub-vertices” sv1, sv2,sv3, sv4, sv5, sv6, sv7, sv8, sv9, sv10 and sv11.

FIG. 25c shows one embodiment of a method to reduce search size bycreating sub-segments. For each sub-vertex, a circular area surroundingthe sub-vertex and having a radius equal to the distance between thesub-vertex and one of the adjacent vertices is analyzed to identifycandidates for locations of interest. Thus, the area surrounding each ofthe sub-vertices sv1, sv2, sv3, sv4, sv5, sv6, sv7, sv8, sv9, sv10 andsv11 that is used to find candidates for locations of interest (144)shown in FIG. 25c . As seen from FIG. 25c , the gas station (15) isincluded in more than one circle whereas the gas station (14) ispartially included in one of the circles whereas the convenience store(11) further away from the area covered by the circle centered at sv3.Therefore, candidate locations can be ordered based on the extent oftheir coverage within the area that falls below the route threshold.Thus, the order of locations for associating with location basedservices based on their distance from the selected route (1106) is l5,l2, and l1.

FIG. 26 shows a formula 2602 that can be used to calculate a weightedradius that is based on the angle at a vertex in addition to the lengthof the route segment (rs) and the route threshold (rt). This is incontrast to the Eq. (1) discussed above which is only based on thelength of the route segment (rs) and the route threshold (rt). As seenin FIG. 26 various values for the weighted radius (wr) are shown whenthe selected route (2202) is bent at different angles.

FIGS. 27a, 27b, 27c, 27d and 27e illustrate various possible angles forthe selected route (2202). Accordingly, the value of the weighted radiusvaries. At FIG. 27a the selected route (2202) is bent at or the angle atthe vertex of the bend is 0 degrees the value of the weighted radius(wr) is 2.5 units. FIG. 27b , illustrates a case when the angle at thevertex is 135 degrees the value of the weighted radius (wr) is 1.9052units. In FIG. 27c , the angle at the vertex is 45 degrees the value ofthe weighted radius (wr) is 2.3979 units. In FIG. 27d , the angle at thevertex is 90 degrees the value of the weighted radius (wr) is 2.1514units. The point D is approximately at a distance of 2.1514 units to thevertex B and an establishment located at point D can be considered asone of the locations of interest (144) to the user (101) that is inproximity to the selected route (2202). FIG. 27e , illustrates a casewhen the angle at the vertex is 180 degrees the value of the weightedradius (wr) is 1.8028. This is a special case that usually occurs onhighways where the user (101) travels on a long straight route which haslittle or no angular deviations or vertices. Therefore, considering amid-point of a long route can cover a very large area to identifylocations of interest (144) which may be impractical. Therefore, theselected route (2202) in this case is analyzed by further dividing intoshorter route segments, for example, at the highway exit points whichcan be considered as the route vertices which are 180 degrees apart.This facilitates analyzing the selected route (2202) to identifylocations of interest (144) to the user (101) that are proximate to thehighway exits. Hence, the area that is close to the selected route(2202) is covered by the route analyzer (1244).

FIG. 28 illustrates a map 2800 wherein the convenience store (locationl1), a first gas station (location l2) and a second gas station(location l5) are identified as candidates to be considered as thelocations of interest (144) to the user (101) along the selected route(1106). In particular, the area (2702) that is close to the selectedroute (1106) is demarcated by the outline (2002) and only locationslying within the area (2004) are considered as candidate locations.Accordingly, the outdoor mall (location 13) and the third gas station(location l4) which at least partially lie beyond the boundary of theoutline (2002) are not considered as candidates for locations ofinterest (144) to the user. It may be appreciated that FIG. 28 and thediscussion herein is only by the way of illustration and not limitation.Other factors such as user specific profile (131) can affect theselection of candidates for location of interest (144) so that locationsthat at least partially lie within the boundary of the outline (2002)can also be considered as candidate locations in accordance with certainembodiments.

FIG. 29 illustrates an embodiment related to further determinations ofproximity for the candidate locations l1, l2 and l5 for determining oneor more of the candidate locations where the user (101) is likely tospend time and therefore is a good candidate for providing locationbased services can be identified. In an embodiment, the locations l1, l2and l5 can be ordered based on their respective distances from theselected route (1106) and their order of distances from the selectedroute (1106) can be an attribute that is considered for providingdifferent types location based services. In an embodiment, the candidatelocations l1, l2 and l5 can be further filtered so that only the closestlocation or two of the closest locations can be used for real-timemessaging or location based services. FIG. 23 shows example methods thatcan be employed for determining respective distances of the candidatelocations l1 and l5 from the selected route (1106). In this embodiment,the criterion for selecting a location as one of the locations ofinterest (144) is the distance of that location from the selected route(1106). Therefore, the area (2104) enclosed within the outline (2102) isfurther divided into isolated bubbles surrounding the candidatelocations l1, l2 and l5 as shown in FIG. 27 and the respective distancesof the candidate locations l1 and l5 from the selected route (1106) aredetermined.

In an embodiment, the method of determining the distances of thecandidate locations l1, l2 and l5 to the selected route (1106) commenceswith identifying the route vertices that are nearest to one of thecandidate locations l1, l2 and l5. Route vertices v1 and v2 are closestto the location l1. Knowing the distances of the route vertices v1 andv2 to the location l1, and using the law of sines, law of cosines it canbe determined that the route vertices v1 and v2 form an oblique trianglewith the location l1. Accordingly, the route vertices v1 and v2 can formone of the two oblique triangles 2902 and 2904 with the location l1. Inthis embodiment, the perpendicular distance (d1) between a referencepoint R on the selected route (1106) that and the convenience store (l1)and a point on the selected route (1106) that is closest to the location(l1) can be considered as the distance of the convenience store from theselected route (1106).

Similarly, it can be identified that route vertices v3 and v4 which areclosest to the location l5 and the triangle formed by the three pointsv3, v4 and l5 is a non-oblique triangle (2906). Therefore, two righttriangles (2906) and (2908) can be created and solved for the nearestside to obtain the shortest distance of the gas station location l5 fromthe selected route (1106). Based on various other criteria that may bedefined in the portal (143) for example, user preferences comprised inthe user specific profile (131) either one of or both the locations l1and l5 can be selected for providing location based services.

FIG. 30 shows a flow chart 3000 that details a method of determining thelocations of interest (144) to the user (101) along the route (1106)taking into consideration the affinities that the user (101) has forparticular merchants/locations. The method begins at 3002 withgeneration of a prediction regarding the user's (101) likely route(1106) or reception of information regarding a selected route (1106) ofthe user (101). At 3004, the locations, merchants and/or offers alongthe user's (101) route (1106) are obtained. In an embodiment, all themerchants, locations or offers can be selected. In an embodiment, themerchants/locations/offers can be filtered at 3004 based on generalcontext data such as, date, day of the week or time of the day,particulars of the offers or user-specific context data such as profileinformation. At 3006 a point is selected for measuring the distances ofthe locations and at 3008 the distances of the locations are measuredfrom the selected point. In an embodiment, the selected point is thestarting point of the route or the origin and the distances aredetermined from the origin ls. In an embodiment the distances of thelocations are determined from the destination le, wherein thedestination can be selected or predicted in accordance with embodimentsdescribe herein. The point from which the distances are measured can beselected depending on various user-specific and/or route-specificfactors such as proximity of the user (101) to the origin (ls), userhabits such as user providing reviews for a particular location. Forexample, if the user (101) has travelled past the mid-way point of theroute (1106) then offers/locations closer to the destination might beselected. At 3010, the affinity values of the user for each of thelocations are retrieved. If the affinity values are not readilyavailable, they can be determined from other user specific profileinformation (131). As discussed herein, the affinity values can berepresented as numbers within a certain range, for example from 0 to 1,with zero (0) representing the least affinity of the user for a givenlocation, merchant, offer or combinations thereof and one (1)representing the highest affinity of the user for a given location,merchant, offer or combinations thereof.

At 3012, the affinity values determined at 3010 are compared. If it isconcluded at 3012 that the user (101) has equal affinity for all thelocations for which the affinity values are obtained, then locationsthat are proximate to the selected point or within a certain predefinedthreshold distance from the selected point are chosen at 3014. Forexample, all the locations within a certain threshold distance can beselected at 3014 in an embodiment. In an embodiment, the locations canbe ranked in an ascending order of their distances to the selected pointand the top N locations (N being a natural number) can be selected at3014 as locations that are proximate to the selected point and presentedto the user (101) at 3020. In an embodiment, location based services areassociated with the locations/merchants that are presented to the userat 3020.

If at 3012, it is determined that different locations have differentaffinities for the user (101), a utility for each location is obtainedat 3016 by weighing the affinities of each of the locations with thedistance of the locations from the selected point. By the way of anon-limiting embodiment, utility can be calculated as a product of theaffinity with an inverse of the distance obtained at 3008. At 3018, thelocations are ranked by their utility, for example, in a descendingorder of their respective utilities and the location with highestutility is selected for presentation the user at 3020 in accordance withan embodiment. In another embodiment, the top N locations (N being anatural number) with the highest utility values are selected forpresentation to the user (101) so that the user (101) may further chooseany of the presented offers/locations.

FIG. 31 shows a map including the route (1106) and which shows thedistances of the route segments between the origin (ls) and thedestination (le). It may be noted that the distances are shown for thevarious route segments rs1 to rs6 (or segments A to H) in terms ofkilometers, for example, in contrast to the segment lengths discussed inFIG. 15 which are taken to scale while analyzing locations on the route(1106). In particular, the total length of the route (1106) is the sumof the route segments, which is:

A+B+C+D+E+F+G+H=0.1+0.3+0.6+0.5+1.0+0.6+1.0+0.1=4.2 Kilometers.

Based on context information as set forth above, two locations, l2(valero 1) and l4 (valero 2) are identified for further promotion to theuser (101). In an embodiment, if both the locations l2 and l4 have equalaffinity for the user (101), the location l2 which is closer to theorigin ls is presented to the user (101) if the user is between theorigin ls and a route mid-point as described supra. In an embodiment, ifboth the locations l2 and l4 have equal affinity for the user (101), thelocation l4 which is closer to the destination le is presented to theuser (101) if the user is past the a route mid-point and is closer tothe destination le.

In an embodiment, if both the locations have different affinities, theutility values are estimated for each location by weighing theaffinities with the respective distances of the locations, l2 (valero 1)and l4 (valero 2) from a selected point. Following describes anon-limiting embodiment of calculating utility values for locationshaving unequal affinities for the user (101). For example, the firstlocation l2 (valero 1) has an affinity of 0.5 and l4 (valero 2) has anaffinity of 0.7 indicating that the user (101) initially has greateraffinity for 14 (valero 2). Based on the map, following are the utilityvalues when calculated as a product of the affinity with the inverse ofthe respective distances of the locations l2 and l4 from the selectedpoint which in this case is the origin ls:

Utility for l2 (valero 1)=1/1.4 Km*0.5 aff=0.357 utility,

Utility for l4 (valero 2)=1/2.1 Km*0.7 aff=0.333 utility.

As the utility for location l2 (valero 1) is higher, it is selected forpresentation to the user even though the affinity of the user (101) forthis location is lower than the affinity for location l4 (valero 2). Itmay be appreciated that the details of the utility value calculationsare shown only by the way of illustration and that the affinity can beweighed with any function that increases as the distance decreases.

FIG. 32 shows a flow chart 3000 that details a method of determining thelocations (144) that are of interest to the user (101) along the route(1106) and proximate to the route, or more particularly, the routesegments, taking into consideration the affinities that the user (101)has for particular merchants/locations. The method begins at 3202 withgeneration of a prediction regarding the user's (101) likely route(1106) or reception of information regarding a selected route (1106) ofthe user (101). At 3204, the locations, merchants and/or offers alongthe user's (101) route (1106) are obtained in accordance to embodimentsdetailed supra. In an embodiment, all the merchants, locations or offerscan be selected. In an embodiment, the merchants/locations/offers can befiltered at 3204 based on general context data such as, date, day of theweek or time of the day, particulars of the offers or user-specificcontext data such as profile information. At 3206, the distances of thelocations obtained at 3204 are measured from the route (1106) or moreparticularly, the distances of the locations are measured from thesegments of the route (1106) that are closest to such locations. Forexample, the distance of the location l2 is measured from the routesegment rs3. At 3208, the affinity values of the user (101) for each ofthe locations are retrieved. If the affinity values are not readilyavailable, they can be determined from other user specific profileinformation (131). In an embodiment, the affinity values can berepresented as numbers within a certain range, for example from 0 to 1,with zero (0) representing the least affinity of the user for a givenlocation, merchant, offer or combinations thereof and one (1)representing the highest affinity of the user for a given location,merchant, offer or combinations thereof.

At 3210, the affinity values determined at 3208 for different locations,offers or merchants are compared. If it is determined at 3210 that theuser (101) has equal affinity for all the locations for which theaffinity values were obtained, then locations that are proximate to theroute (1106) are chosen at 3214. In an embodiment, the locations withina predefined threshold distance of their respective route segments canbe selected at 3214 as being proximate to the route (1106). In anembodiment, the locations can be ranked in an ascending order of theirdistances to their respective route segments so that the top N locations(N being a natural number) can be selected at 3214 and presented to theuser (101) at 3218. In an embodiment, location based services areassociated with the locations/merchants that are presented to the userat 3218.

If at 3210, it is determined that different locations have differentaffinities for the user (101), a utility for each location is obtainedat 3212 by weighing the affinities of each of the locations with thedistance of the locations from their respective route segments. By theway of a non-limiting embodiment, utility of a location can becalculated as a product of its affinity with an inverse of the distanceobtained at 3206. At 3216, the locations are ranked for example, in adescending order of their respective utilities, and the location(s) withhighest utility is selected for presentation the user at 3218 inaccordance with an embodiment. In another embodiment, the top Nlocations (N being a natural number) with the highest utility values areselected for presentation to the user (101) so that the user (101) mayfurther choose any of the presented offers/locations.

FIG. 33 shows a map with the locations l2, l4, l5, along the route(1106) obtained for further promotion to the user. In an embodiment, oneor more of the locations l2, l4, l5 can be selected upon furtheranalysis for associating location based services such as presenting apromotional offer to the user. In this particular example all the threelocations l2, l4, l5 lie along the route segment rs3. However, it may beappreciated that this is not necessary and that the locations may becloser to other segments of the route (1106) and such locations will beconsidered for presentation to the user based on their respectivedistances from their respective proximate route segments. Based oninformation such as, context information as set forth above, threelocations, l2 (valero 1), l4 (valero 2) and l5 (arco 1) are identifiedfor further promotion to the user (101). In an embodiment, the user(101) has equal affinity for Valero and Arco brands and hence all thelocations l2, l4 and l5 have equal affinity for the user (101).Therefore, the location l5 (arco 1) which at 0.1 Km from the routesegment (rs3) is selected as it is closest to the route segment (rs3)when compared to the location l2 (valero 1) which is at a distance of0.2 Km from the route segment (rs3) and the location l4 (valero 2) whichis at a distance of 0.3 Km from the route segment (rs3).

In an embodiment, the locations l2, l4 and l5 have different affinitiesand hence the selection of location(s) for presentation to the user(101) is based on their utility values. In an embodiment, the utilityvalues are estimated for each location by weighing the affinities withthe respective distances of the locations l2, l4 and l5 from the routesegment (rs3). Following describes a non-limiting embodiment ofcalculating utility values for locations having unequal affinities forthe user (101). For example, the user (101) has an affinity of 0.25 forArco brand and an affinity of 1.0 for Valero brand. Based on the map,following are the utility values when calculated as a product of theaffinity with the inverse of the respective distances of the locationsl2, l4 and l5 from the route segment (rs3):

Utility for l2 (valero 1)=1/0.2 Km*1 affinity=5 utility,

Utility for l4 (valero 2)=1/0.3 Km*1 affinity=3.33 utility, and

Utility for l5 (arco1)=1/0.1 Km*0.25 affinity=2.5 utility.

As the utility for location l2 (valero 1) is highest compared to otherlocations l4 and l5, location l2 (valero 1) is selected for presentationto the user (101) even when the location l5 is closest to the routesegment (rs3). It may be appreciated that the details of the utilityvalue calculations are shown only by the way of illustration and thatthe affinity can be weighed with any function that increases as thedistance decreases for the estimation of the utility.

FIG. 34 shows a method to provide benefits according to one embodiment.In FIG. 34, the computing apparatus is configured to generate (231) atrigger record (207) for a transaction handler (103) to identify anauthorization request (202) that satisfies the conditions specified inthe trigger record (207) for an offer (186) associated with an accountidentifier (e.g., account data (111), account information (142), oraccount number (302)).

In FIG. 34, the computing apparatus is configured to identify (233) theauthorization request (202) of a transaction according to the triggerrecord (207) and determine (235) whether the transaction, if completed,satisfies the conditions required for the qualification of a benefit ofthe offer (186) in accordance with the offer rules (203).

If the transaction satisfies (237) the benefit qualification conditionsin accordance with the offer rules (203) of the offer (186), thecomputing apparatus is configured to transmit (239), to a communicationreference (205) associated with the account identifier (e.g., accountdata (111), account information (142), or account number (302)), amessage (204) to identify the qualification. The computing apparatus isconfigured to further generate (241) a trigger record (207) for thetransaction handler (103) to identify a settlement request for thetransaction. If the transaction is settled (243), the computingapparatus is configured to provide (245) the benefit of the offer (186)to a consumer account (146) identified by the account identifier (e.g.,account data (111), account information (142), or account number (302))via statement credit. In one embodiment, the statement credit isprovided as part of the settlement operations of the transaction.

In one embodiment, a computer-implemented method includes: storing, in acomputing apparatus having a transaction handler (103), a plurality oftrigger records (207); processing, by the transaction handler (103), anauthorization request (202) received from an acquirer processor (147),where the authorization request (202) is processed for a payment to bemade by an issuer processor (145) on behalf of a user (101) having anaccount identifier (e.g., account data (111), account information (142),or account number (302)) associated with the issuer processor (145), andthe acquirer processor (147) is configured to receive the payment onbehalf of a merchant operating the transaction terminal (105).

In one embodiment, the method further includes: determining, by thetransaction handler (103), whether the authorization request (202)matches one of the plurality of trigger records (207) by determiningwhether the attributes of the transaction associated with theauthorization request (202) satisfies the conditions specified in one ofthe plurality of trigger records (207).

In one embodiment, if the authorization request (202) matches a triggerrecord (207) in the plurality of the trigger records (207), thecomputing apparatus is configured to identify a communication reference(205) of the user (101) in accordance with the trigger record (207),generate a message (204) regarding a benefit to be provided to the user(101) upon the completion of the payment, and transmit the message (204)to the user (101) via the communication reference (205) in real-timewith the processing of the authorization request (202). In oneembodiment, the communication reference (205) is one of: a phone numberand an email address; and the message (204) is transmitted via at leastone of: short message service and email.

In one embodiment, the message (204) is transmitted to a mobile phone ofthe user (101) via the communication reference (205).

In one embodiment, the message (204) is transmitted to the user (101)via a communication channel separate from a communication channel usedto provide a response (206) to the authorization request (202).

In one embodiment, the method further includes the computing apparatusidentifying an offer (186) based on transaction data (109) of the user;and the message (204) is configured to provide the offer (186).

In one embodiment, the computing apparatus includes the portal (143)configured to receive offer rules (203) from a merchant for the offer(186); and the offer (186) is identified for delivery in the real-timemessage (204) based further on the offer rules (203).

In one embodiment, the offer (186) is identified in real-time with theprocessing of the authorization request (202), or in response to adetermination that the authorization request (202) matches the triggerrecord (207).

In one embodiment, the offer (186) is identified based on a profile(e.g., 131, or 341) of the user (101). In one embodiment, the profile(e.g., 131 or 341) summarizes the transaction data (109) of the user(101). In one embodiment, the computing apparatus includes the profilegenerator (121) configured to generate the profile (e.g., 341) from thetransaction data (109) of the user (101) via a cluster analysis (329)and a factor analysis (327), as described in the section entitled“AGGREGATED SPENDING PROFILE.”

In one embodiment, the message (204) indicates that a transaction forwhich the authorization request (202) is processed is eligible for thebenefit of an offer (186) associated with the account identifier (e.g.,account data (111)) of the user (101), when the transaction iseventually completed and settled.

In one embodiment, the offer (186) is stored in the data warehouse (149)in association with the account identifier (e.g., account data (111));and the trigger record (207) identifies the offer (186) to allow themessage broker (201) to further check whether the transaction meets thebenefit redemption conditions of the offer (186).

In one embodiment, the computer apparatus is configured to determinewhether the payment, if completed, entitles the user (101) to thebenefit of the offer (186), in response to a determination that theauthorization request (202) matches the trigger record (207); and themessage (204) is transmitted to the user (101) via the communicationreference (205) in response to an indication of the approval of theauthorization request (202) and after a determination is made that thepayment, if completed, entitles the user (101) to the benefit of theoffer (186).

In one embodiment, the transaction handler (103) is configured toidentify a settled transaction corresponding to the authorizationrequest (202) that triggers the message (204), and then provide thebenefit of the offer (186) to the user (101) via statement credits, orloyalty program points, after the settled transaction is identified.

In one embodiment, the transaction handler (103) is configured toprovide the benefit of the offer (186) to the user (101) via point ofsale credit using digital coupons transmitted to cellular telephone ofthe user (101) during the processing of the payment at the transactionterminal (105).

In one embodiment, the transaction handler (103) is configured toprocess a settlement request for the payment and provide the benefit ofthe offer (186) to the user (101) via statement credit to a consumeraccount (146) corresponding to the account identifier (e.g., accountdata (111)) in response to the completion of the settlement of thepayment, or as part of the settlement of the payment.

In one embodiment, the computing apparatus is configured to generate asecond trigger record for the transaction handler (103) to monitor thesettlement of the payment, in order to provide a benefit in response tothe settlement of the payment, or as part of the settlement of thepayment.

In one embodiment, the computing apparatus includes: a data warehouse(149) configured to store a plurality of trigger records (207); atransaction handler (103) coupled with the data warehouse (149) andconfigured to process an authorization request (202) received from anacquirer processor (147); and a message broker (201) coupled with thetransaction handler (103) such that after the transaction handler (103)determines that the authorization request (202) matches a trigger record(207) in the plurality of the trigger records (207), the message broker(201) identifies a communication reference (205) of the user (101) inaccordance with the trigger record (207) and generates a message (204)regarding a benefit to be provided to the user (101) upon completion ofthe payment. The computing apparatus further includes a media controller(115) coupled with the message broker (201) to transmit the message(204) to the user (101) via the communication reference (205) inreal-time with the transaction handler (103) processing theauthorization request (202).

Details about the system in one embodiment are provided in the sectionentitled “CENTRALIZED DATA WAREHOUSE” and “HARDWARE.”

Variations

Some embodiments use more or fewer components than those illustrated inthe figures.

In one embodiment, at least some of the profile generator (121),correlator (117), profile selector (129), and advertisement selector(133) are controlled by the entity that operates the transaction handler(103). In another embodiment, at least some of the profile generator(121), correlator (117), profile selector (129), and advertisementselector (133) are not controlled by the entity that operates thetransaction handler (103).

In one embodiment, the products and/or services purchased by the user(101) are also identified by the information transmitted from themerchants or service providers. Thus, the transaction data (109) mayinclude identification of the individual products and/or services, whichallows the profile generator (121) to generate transaction profiles(127) with fine granularity or resolution. In one embodiment, thegranularity or resolution may be at a level of distinct products andservices that can be purchased (e.g., stock-keeping unit (SKU) level),or category or type of products or services, or vendor of products orservices, etc.

In one embodiment, the entity operating the transaction handler (103)provides the intelligence information in real time as the request forthe intelligence information occurs. In other embodiments, the entityoperating the transaction handler (103) may provide the intelligenceinformation in batch mode. The intelligence information can be deliveredvia online communications (e.g., via an application programminginterface (API) on a website, or other information server), or viaphysical transportation of a computer readable media that stores thedata representing the intelligence information.

In one embodiment, the intelligence information is communicated tovarious entities in the system in a way similar to, and/or in parallelwith the information flow in the transaction system to move money. Thetransaction handler (103) routes the information in the same way itroutes the currency involved in the transactions.

In one embodiment, the portal (143) provides a user interface to allowthe user (101) to select items offered on different merchant websitesand store the selected items in a wish list for comparison, reviewing,purchasing, tracking, etc. The information collected via the wish listcan be used to improve the transaction profiles (127) and deriveintelligence on the needs of the user (101); and targeted advertisementscan be delivered to the user (101) via the wish list user interfaceprovided by the portal (143). Examples of user interface systems tomanage wish lists are provided in U.S. patent application Ser. No.12/683,802, filed Jan. 7, 2010, assigned U.S. Pat. App. Pub. No.2010/0174623, and entitled “System and Method for Managing Items ofInterest Selected from Online Merchants,” the disclosure of which ishereby incorporated herein by reference.

Aggregated Spending Profile

In one embodiment, the characteristics of transaction patterns ofcustomers are profiled via clusters, factors, and/or categories ofpurchases. The transaction data (109) may include transaction records(301); and in one embodiment, an aggregated spending profile (341) isgenerated from the transaction records (301), in a way illustrated inFIG. 2, to summarize the spending behavior reflected in the transactionrecords (301).

In FIG. 2, each of the transaction records (301) is for a particulartransaction processed by the transaction handler (103). Each of thetransaction records (301) provides information about the particulartransaction, such as the account number (302) of the consumer account(146) used to pay for the purchase, the date (303) (and/or time) of thetransaction, the amount (304) of the transaction, the ID (305) of themerchant who receives the payment, the category (306) of the merchant,the channel (307) through which the purchase was made, etc. Examples ofchannels include online, offline in-store, via phone, etc. In oneembodiment, the transaction records (301) may further include a field toidentify a type of transaction, such as card-present, card-not-present,etc.

A “card-present” transaction typically involves physically presentingthe account identification device (141), such as a financial transactioncard, to the merchant (e.g., via swiping a credit card at a POS terminalof a merchant); and a “card-not-present” transaction typically involvespresenting the account information (142) of the consumer account (146)to the merchant to identify the consumer account (146) withoutphysically presenting the account identification device (141) to themerchant or the transaction terminal (105).

The transaction records (301) of one embodiment may further includedetails about the products and/or services involved in the purchase.

When there is voluminous data representing the transaction records(301), the spending patterns reflected in the transaction records (301)can be difficult to recognize by an ordinary person.

In FIG. 2, the voluminous transaction records (301) are summarized (335)into aggregated spending profiles (e.g., 341) to concisely present thestatistical spending characteristics reflected in the transactionrecords (301). The aggregated spending profile (341) uses values derivedfrom statistical analysis to present the statistical characteristics oftransaction records (301) of an entity in a way easy to understand by anordinary person.

In FIG. 2, the transaction records (301) are summarized (335) via factoranalysis (327) to condense the variables (e.g., 313, 315) and viacluster analysis (329) to segregate entities by spending patterns.

In FIG. 2, a set of variables (e.g., 311, 313, 315) are defined based onthe parameters recorded in the transaction records (301). The variables(e.g., 311, 313, and 315) are defined in a way to have meanings easilyunderstood by an ordinary person. For example, variables (311) measurethe aggregated spending in super categories; variables (313) measure thespending frequencies in various areas; and variables (315) measure thespending amounts in various areas. In one embodiment, each of the areasis identified by a merchant category (306) (e.g., as represented by amerchant category code (MCC), a North American Industry ClassificationSystem (NAICS) code, or a similarly standardized category code). Inother embodiments, an area may be identified by a product category, aSKU number, etc.

Examples of the spending frequency variables (313) and spending amountvariables (315) defined for various merchant categories (e.g., 306) inone embodiment are provided in U.S. patent application Ser. No.12/537,566, filed Aug. 7, 2009, assigned U.S. Pat. App. Pub. No.2010/0306029, and entitled “Cardholder Clusters,” and in U.S. patentapplication Ser. No. 12/777,173, filed May 10, 2010, assigned U.S. Pat.App. Pub. No. 2010/0306032, and entitled “Systems and Methods toSummarize Transaction Data,” the disclosures of which applications arehereby incorporated herein by reference.

In FIG. 2, the aggregation (317) includes the application of thedefinitions (309) for these variables (e.g., 311, 313, and 315) to thetransaction records (301) to generate the variable values (321). Thetransaction records (301) are aggregated to generate aggregatedmeasurements (e.g., variable values (321)) that are not specific to aparticular transaction, such as frequencies of purchases made withdifferent merchants or different groups of merchants, the amounts spentwith different merchants or different groups of merchants, and thenumber of unique purchases across different merchants or differentgroups of merchants, etc. The aggregation (317) can be performed for aparticular time period and for entities at various levels.

The transaction records (301) can be aggregated according to a buyingentity, or a selling entity. For example, the aggregation (317) can beperformed at account level, person level, family level, company level,neighborhood level, city level, region level, etc. to analyze thespending patterns across various areas (e.g., sellers, products orservices) for the respective aggregated buying entity. For example, thetransaction records (301) for a particular merchant having transactionswith multiple accounts can be aggregated for a merchant level analysis.For example, the transaction records (301) for a particular merchantgroup can be aggregated for a merchant group level analysis. Theaggregation (317) can be formed separately for different types oftransactions, such as transactions made online, offline, via phone,and/or “card-present” transactions vs. “card-not-present” transactions,which can be used to identify the spending pattern differences amongdifferent types of transactions.

In FIG. 2, the variable values (e.g., 323, 324, . . . , 325) associatedwith an entity ID (322) are considered the random samples of therespective variables (e.g., 311, 313, 315), sampled for the instance ofan entity represented by the entity ID (322). Statistical analyses(e.g., factor analysis (327) and cluster analysis (329)) are performedto identify the patterns and correlations in the random samples.

Once the cluster definitions (333) are obtained from the clusteranalysis (329), the identity of the cluster (e.g., cluster ID (343))that contains the entity ID (322) can be used to characterize spendingbehavior of the entity represented by the entity ID (322). The entitiesin the same cluster are considered to have similar spending behaviors.

In FIG. 2, the random variables (e.g., 313 and 315) as defined by thedefinitions (309) have certain degrees of correlation and are notindependent from each other. For example, merchants of differentmerchant categories (e.g., 306) may have overlapping business, or havecertain business relationships. For example, certain products and/orservices of certain merchants have cause and effect relationships. Forexample, certain products and/or services of certain merchants aremutually exclusive to a certain degree (e.g., a purchase from onemerchant may have a level of probability to exclude the user (101) frommaking a purchase from another merchant). Such relationships may becomplex and difficult to quantify by merely inspecting the categories.Further, such relationships may shift over time as the economy changes.

In FIG. 2, a factor analysis (327) is performed to reduce the redundancyand/or correlation among the variables (e.g., 313, 315). The factoranalysis (327) identifies the definitions (331) for factors, each ofwhich represents a combination of the variables (e.g., 313, 315). Afactor from the factor analysis (327) is a linear combination of aplurality of the aggregated measurements (e.g., variables (313, 315))determined for various areas (e.g., merchants or merchant categories,products or product categories). Once the relationship between thefactors and the aggregated measurements is determined via factoranalysis, the values for the factors can be determined from the linearcombinations of the aggregated measurements and be used in a transactionprofile (127 or 341) to provide information on the behavior of theentity represented by the entity ID (e.g., an account, an individual, afamily).

Once the factor definitions (331) are obtained from the factor analysis(327), the factor definitions (331) can be applied to the variablevalues (321) to determine factor values (344) for the aggregatedspending profile (341). Since redundancy and correlation are reduced inthe factors, the number of factors is typically much smaller than thenumber of the original variables (e.g., 313, 315). Thus, the factorvalues (344) represent the concise summary of the original variables(e.g., 313, 315).

For example, there may be thousands of variables on spending frequencyand amount for different merchant categories; and the factor analysis(327) can reduce the factor number to less than one hundred (and evenless than twenty). In one example, a twelve-factor solution is obtained,which allows the use of twelve factors to combine the thousands of theoriginal variables (313, 315); and thus, the spending behavior inthousands of merchant categories can be summarized via twelve factorvalues (344). In one embodiment, each factor is combination of at leastfour variables; and a typical variable has contributions to more thanone factor.

In FIG. 2, an aggregated spending profile (341) for an entityrepresented by an entity ID (e.g., 322) includes the cluster ID (343)and factor values (344) determined based on the cluster definitions(333) and the factor definitions (331). The aggregated spending profile(341) may further include other statistical parameters, such asdiversity index (342), channel distribution (345), category distribution(346), zip code (347), etc., as further discussed below.

In general, an aggregated spending profile (341) may include more orfewer fields than those illustrated in FIG. 2. For example, in oneembodiment, the aggregated spending profile (341) further includes anaggregated spending amount for a period of time (e.g., the past twelvemonths); in another embodiment, the aggregated spending profile (341)does not include the category distribution (346); and in a furtherembodiment, the aggregated spending profile (341) may include a set ofdistance measures to the centroids of the clusters.

FIG. 3 shows a method to generate an aggregated spending profileaccording to one embodiment. In FIG. 3, computation models areestablished (351) for variables (e.g., 311, 313, and 315). In oneembodiment, the variables are defined in a way to capture certainaspects of the spending statistics, such as frequency, amount, etc.

In FIG. 3, data from related accounts are combined (353);recurrent/installment transactions are combined (355); and account dataare selected (357) according to a set of criteria related to activity,consistency, diversity, etc.

In FIG. 3, the computation models (e.g., as represented by the variabledefinitions (309)) are applied (359) to the remaining account data(e.g., transaction records (301)) to obtain data samples for thevariables. The data points associated with the entities, other thanthose whose transactions fail to meet the minimum requirements foractivity, consistency, diversity, etc., are used in factor analysis(327) and cluster analysis (329).

In FIG. 3, the data samples (e.g., variable values (321)) are used toperform (361) factor analysis (327) to identify factor solutions (e.g.,factor definitions (331)). The factor solutions can be adjusted (363) toimprove similarity in factor values of different sets of transactiondata (109).

The data samples can also be used to perform (365) cluster analysis(329) to identify cluster solutions (e.g., cluster definitions (333)).The cluster solutions can be adjusted (367) to improve similarity incluster identifications based on different sets of transaction data(109). For example, cluster definitions (333) can be applied to thetransactions in the time period under analysis (e.g., the past twelvemonths) and be applied separately to the transactions in a prior timeperiod (e.g., the twelve months before the past twelve months) to obtaintwo sets of cluster identifications for various entities. The clusterdefinitions (333) can be adjusted to improve the correlation between thetwo set of cluster identifications.

Optionally, human understandable characteristics of the factors andclusters are identified (369) to name the factors and clusters. Forexample, when the spending behavior of a cluster appears to be thebehavior of an internet loyalist, the cluster can be named “internetloyalist” such that if a cardholder is found to be in the “internetloyalist” cluster, the spending preferences and patterns of thecardholder can be easily perceived.

In one embodiment, the factor analysis (327) and the cluster analysis(329) are performed periodically (e.g., once a year, or six months) toupdate the factor definitions (331) and the cluster definitions (333),which may change as the economy and the society change over time.

In FIG. 3, transaction data (109) are summarized (371) using the factorsolutions and cluster solutions to generate the aggregated spendingprofile (341). The aggregated spending profile (341) can be updated morefrequently than the factor solutions and cluster solutions, when the newtransaction data (109) becomes available. For example, the aggregatedspending profile (341) may be updated quarterly or monthly.

Details about aggregated spending profile (341) in one embodiment areprovided in U.S. patent application Ser. No. 12/777,173, filed May 10,2010, assigned U.S. Pat. App. Pub. No. 2010/0306032, and entitled“Systems and Methods to Summarize Transaction Data,” the disclosure ofwhich is hereby incorporated herein by reference.

In one embodiment, a set of profiles are generated from the transactiondata for a plurality of geographical regions, such as mutuallyexclusive, non-overlapping regions defined by postal codes. Transactionsof account holders residing in the regions are aggregated according tomerchant categories for the respective regions and subsequentlynormalized to obtain preference indicators that reveal the spendingpreferences of the account holders in the respective regions. Each ofthe profiles for respective regions is based on a plurality of differentaccount holders and/or households to avoid revealing private informationabout individual account holders or families. Further, the profiles areconstructed in a way to make it impossible to reverse calculate thetransaction amounts. Further details and examples about profilesconstructed for regions in one embodiment are provided in U.S. patentapplication Ser. No. 13/675,301, filed Nov. 13, 2012, assigned U.S. Pat.App. Pub. No. 2013/0124263, and entitled “Systems and Methods toSummarize Transaction data,” the disclosure of which is herebyincorporated herein by reference.

Transaction Processing and Data

FIG. 4 shows a system to provide information and/or services based ontransaction data (109) according to one embodiment.

In FIG. 4, the transaction handler (103) is coupled between an issuerprocessor (145) and an acquirer processor (147) to facilitateauthorization and settlement of transactions between a consumer account(146) and a merchant account (148). The transaction handler (103)records the transactions in the data warehouse (149). The portal (143)is coupled to the data warehouse (149) to provide information based onthe transaction records (301), such as the transaction profiles (127),aggregated spending profile (341), offer redemption notification, etc.The portal (143) may be implemented as a web portal, a telephonegateway, a file/data server, etc.

In FIG. 4, the transaction terminal (105) initiates the transaction fora user (101) (e.g., a customer) for processing by a transaction handler(103). The transaction handler (103) processes the transaction andstores transaction data (109) about the transaction, in connection withaccount data (111), such as the account profile of an account of theuser (101). The account data (111) may further include data about theuser (101), collected from issuers or merchants, and/or other sources,such as social networks, credit bureaus, merchant provided information,address information, etc. In one embodiment, a transaction may beinitiated by a server (e.g., based on a stored schedule for recurrentpayments).

The accumulated transaction data (109) and the corresponding accountdata (111) are used to generate intelligence information about thepurchase behavior, pattern, preference, tendency, frequency, trend,amount and/or propensity of the users (e.g., 101), as individuals or asa member of a group. The intelligence information can then be used togenerate, identify and/or select targeted advertisements forpresentation to the user (101) on the point of interaction (107), duringa transaction, after a transaction, or when other opportunities arise.

In FIG. 4, the consumer account (146) is under the control of the issuerprocessor (145). The consumer account (146) may be owned by anindividual, or an organization such as a business, a school, etc. Theconsumer account (146) may be a credit account, a debit account, or astored value account. The issuer may provide the consumer (e.g., user(101)) an account identification device (141) to identify the consumeraccount (146) using the account information (142). The respectiveconsumer of the account (146) can be called an account holder or acardholder, even when the consumer is not physically issued a card, orthe account identification device (141), in one embodiment. The issuerprocessor (145) is to charge the consumer account (146) to pay forpurchases.

The account identification device (141) of one embodiment is a plasticcard having a magnetic strip storing account information (142)identifying the consumer account (146) and/or the issuer processor(145). Alternatively, the account identification device (141) is asmartcard having an integrated circuit chip storing at least the accountinformation (142). The account identification device (141) mayoptionally include a mobile phone having an integrated smartcard.

The account information (142) may be printed or embossed on the accountidentification device (141). The account information (142) may beprinted as a bar code to allow the transaction terminal (105) to readthe information via an optical scanner. The account information (142)may be stored in a memory of the account identification device (141) andconfigured to be read via wireless, contactless communications, such asnear field communications via magnetic field coupling, infraredcommunications, or radio frequency communications. Alternatively, thetransaction terminal (105) may require contact with the accountidentification device (141) to read the account information (142) (e.g.,by reading the magnetic strip of a card with a magnetic strip reader).

The transaction terminal (105) is configured to transmit anauthorization request message to the acquirer processor (147). Theauthorization request includes the account information (142), an amountof payment, and information about the merchant (e.g., an indication ofthe merchant account (148)). The acquirer processor (147) requests thetransaction handler (103) to process the authorization request, based onthe account information (142) received in the transaction terminal(105). The transaction handler (103) routes the authorization request tothe issuer processor (145) and may process and respond to theauthorization request when the issuer processor (145) is not available.The issuer processor (145) determines whether to authorize thetransaction based at least in part on a balance of the consumer account(146).

The transaction handler (103), the issuer processor (145), and theacquirer processor (147) may each include a subsystem to identify therisk in the transaction and may reject the transaction based on the riskassessment.

The account identification device (141) may include security features toprevent unauthorized uses of the consumer account (146), such as a logoto show the authenticity of the account identification device (141),encryption to protect the account information (142), etc.

The transaction terminal (105) of one embodiment is configured tointeract with the account identification device (141) to obtain theaccount information (142) that identifies the consumer account (146)and/or the issuer processor (145). The transaction terminal (105)communicates with the acquirer processor (147) that controls themerchant account (148) of a merchant. The transaction terminal (105) maycommunicate with the acquirer processor (147) via a data communicationconnection, such as a telephone connection, an Internet connection, etc.The acquirer processor (147) is to collect payments into the merchantaccount (148) on behalf of the merchant.

In one embodiment, the transaction terminal (105) is a POS terminal at atraditional, offline, “brick and mortar” retail store. In anotherembodiment, the transaction terminal (105) is an online server thatreceives account information (142) of the consumer account (146) fromthe user (101) through a web connection. In one embodiment, the user(101) may provide account information (142) through a telephone call,via verbal communications with a representative of the merchant; and therepresentative enters the account information (142) into the transactionterminal (105) to initiate the transaction.

In one embodiment, the account information (142) can be entered directlyinto the transaction terminal (105) to make payment from the consumeraccount (146), without having to physically present the accountidentification device (141). When a transaction is initiated withoutphysically presenting an account identification device (141), thetransaction is classified as a “card-not-present” (CNP) transaction.

In general, the issuer processor (145) may control more than oneconsumer account (146); the acquirer processor (147) may control morethan one merchant account (148); and the transaction handler (103) isconnected between a plurality of issuer processors (e.g., 145) and aplurality of acquirer processors (e.g., 147). An entity (e.g., bank) mayoperate both an issuer processor (145) and an acquirer processor (147).

In one embodiment, the transaction handler (103), the issuer processor(145), the acquirer processor (147), the transaction terminal (105), theportal (143), and other devices and/or services accessing the portal(143) are connected via communications networks, such as local areanetworks, cellular telecommunications networks, wireless wide areanetworks, wireless local area networks, an intranet, and Internet.Dedicated communication channels may be used between the transactionhandler (103) and the issuer processor (145), between the transactionhandler (103) and the acquirer processor (147), and/or between theportal (143) and the transaction handler (103).

In FIG. 4, the transaction handler (103) uses the data warehouse (149)to store the records about the transactions, such as the transactionrecords (301) or transaction data (109).

Typically, the transaction handler (103) is implemented using a powerfulcomputer, or cluster of computers functioning as a unit, controlled byinstructions stored on a computer readable medium. The transactionhandler (103) is configured to support and deliver authorizationservices, exception file services, and clearing and settlement services.The transaction handler (103) has a subsystem to process authorizationrequests and another subsystem to perform clearing and settlementservices. The transaction handler (103) is configured to processdifferent types of transactions, such credit card transactions, debitcard transactions, prepaid card transactions, and other types ofcommercial transactions. The transaction handler (103) interconnects theissuer processors (e.g., 145) and the acquirer processor (e.g., 147) tofacilitate payment communications.

In FIG. 4, the transaction terminal (105) is configured to submit theauthorized transactions to the acquirer processor (147) for settlement.The amount for the settlement may be different from the amount specifiedin the authorization request. The transaction handler (103) is coupledbetween the issuer processor (145) and the acquirer processor (147) tofacilitate the clearing and settling of the transaction. Clearingincludes the exchange of financial information between the issuerprocessor (145) and the acquirer processor (147); and settlementincludes the exchange of funds.

In FIG. 4, the issuer processor (145) is configured to provide funds tomake payments on behalf of the consumer account (146). The acquirerprocessor (147) is to receive the funds on behalf of the merchantaccount (148). The issuer processor (145) and the acquirer processor(147) communicate with the transaction handler (103) to coordinate thetransfer of funds for the transaction. The funds can be transferredelectronically.

The transaction terminal (105) may submit a transaction directly forsettlement, without having to separately submit an authorizationrequest.

In one embodiment, the portal (143) provides a user interface to allowthe user (101) to organize the transactions in one or more consumeraccounts (146) of the user with one or more issuers. The user (101) mayorganize the transactions using information and/or categories identifiedin the transaction records (301), such as merchant category (306),transaction date (303), amount (304), etc. Examples and techniques inone embodiment are provided in U.S. patent application Ser. No.11/378,215, filed Mar. 16, 2006, assigned U.S. Pat. App. Pub. No.2007/0055597, and entitled “Method and System for Manipulating PurchaseInformation,” the disclosure of which is hereby incorporated herein byreference.

In one embodiment, the portal (143) provides transaction basedstatistics, such as indicators for retail spending monitoring,indicators for merchant benchmarking, industry/market segmentation,indicators of spending patterns, etc. Further examples can be found inU.S. patent application Ser. No. 12/191,796, filed Aug. 14, 2008,assigned U.S. Pat. App. Pub. No. 2009/0048884, and entitled “MerchantBenchmarking Tool,” U.S. patent application Ser. No. 12/940,562, filedNov. 5, 2010, and U.S. patent application Ser. No. 12/940,664, filedNov. 5, 2010, the disclosures of which applications are herebyincorporated herein by reference.

Transaction Terminal

FIG. 5 illustrates a transaction terminal according to one embodiment.The transaction terminal (105) illustrated in FIG. 5 can be used invarious systems discussed in connection with other figures of thepresent disclosure. In FIG. 5, the transaction terminal (105) isconfigured to interact with an account identification device (141) toobtain account information (142) about the consumer account (146).

In one embodiment, the transaction terminal (105) includes a memory(167) coupled to the processor (151), which controls the operations of areader (163), an input device (153), an output device (165) and anetwork interface (161). The memory (167) may store instructions for theprocessor (151) and/or data, such as an identification that isassociated with the merchant account (148).

In one embodiment, the reader (163) includes a magnetic strip reader. Inanother embodiment, the reader (163) includes a contactless reader, suchas a radio frequency identification (RFID) reader, a near fieldcommunications (NFC) device configured to read data via magnetic fieldcoupling (in accordance with ISO standard 14443/NFC), a Bluetoothtransceiver, a WiFi transceiver, an infrared transceiver, a laserscanner, etc.

In one embodiment, the input device (153) includes key buttons that canbe used to enter the account information (142) directly into thetransaction terminal (105) without the physical presence of the accountidentification device (141). The input device (153) can be configured toprovide further information to initiate a transaction, such as apersonal identification number (PIN), password, zip code, etc. that maybe used to access the account identification device (141), or incombination with the account information (142) obtained from the accountidentification device (141).

In one embodiment, the output device (165) may include a display, aspeaker, and/or a printer to present information, such as the result ofan authorization request, a receipt for the transaction, anadvertisement, etc.

In one embodiment, the network interface (161) is configured tocommunicate with the acquirer processor (147) via a telephoneconnection, an Internet connection, or a dedicated data communicationchannel.

In one embodiment, the instructions stored in the memory (167) areconfigured at least to cause the transaction terminal (105) to send anauthorization request message to the acquirer processor (147) toinitiate a transaction. The transaction terminal (105) may or may notsend a separate request for the clearing and settling of thetransaction. The instructions stored in the memory (167) are alsoconfigured to cause the transaction terminal (105) to perform othertypes of functions discussed in this description.

In one embodiment, a transaction terminal (105) may have fewercomponents than those illustrated in FIG. 5. For example, in oneembodiment, the transaction terminal (105) is configured for“card-not-present” transactions; and the transaction terminal (105) doesnot have a reader (163).

In one embodiment, a transaction terminal (105) may have more componentsthan those illustrated in FIG. 5. For example, in one embodiment, thetransaction terminal (105) is an ATM machine, which includes componentsto dispense cash under certain conditions.

Account Identification Device

FIG. 6 illustrates an account identifying device according to oneembodiment. In FIG. 6, the account identification device (141) isconfigured to carry account information (142) that identifies theconsumer account (146).

In one embodiment, the account identification device (141) includes amemory (167) coupled to the processor (151), which controls theoperations of a communication device (159), an input device (153), anaudio device (157) and a display device (155). The memory (167) maystore instructions for the processor (151) and/or data, such as theaccount information (142) associated with the consumer account (146).

In one embodiment, the account information (142) includes an identifieridentifying the issuer (and thus the issuer processor (145)) among aplurality of issuers, and an identifier identifying the consumer accountamong a plurality of consumer accounts controlled by the issuerprocessor (145). The account information (142) may include an expirationdate of the account identification device (141), the name of theconsumer holding the consumer account (146), and/or an identifieridentifying the account identification device (141) among a plurality ofaccount identification devices associated with the consumer account(146).

In one embodiment, the account information (142) may further include aloyalty program account number, accumulated rewards of the consumer inthe loyalty program, an address of the consumer, a balance of theconsumer account (146), transit information (e.g., a subway or trainpass), access information (e.g., access badges), and/or consumerinformation (e.g., name, date of birth), etc.

In one embodiment, the memory includes a nonvolatile memory, such asmagnetic strip, a memory chip, a flash memory, a Read Only Memory (ROM),etc. to store the account information (142).

In one embodiment, the information stored in the memory (167) of theaccount identification device (141) may also be in the form of datatracks that are traditionally associated with credits cards. Such tracksinclude Track 1 and Track 2. Track 1 (“International Air TransportAssociation”) stores more information than Track 2, and contains thecardholder's name as well as the account number and other discretionarydata. Track 1 is sometimes used by airlines when securing reservationswith a credit card. Track 2 (“American Banking Association”) iscurrently most commonly used and is read by ATMs and credit cardcheckers. The ABA (American Banking Association) designed thespecifications of Track 1 and banks abide by it. It contains thecardholder's account number, encrypted PIN, and other discretionarydata.

In one embodiment, the communication device (159) includes asemiconductor chip to implement a transceiver for communication with thereader (163) and an antenna to provide and/or receive wireless signals.

In one embodiment, the communication device (159) is configured tocommunicate with the reader (163). The communication device (159) mayinclude a transmitter to transmit the account information (142) viawireless transmissions, such as radio frequency signals, magneticcoupling, or infrared, Bluetooth or WiFi signals, etc.

In one embodiment, the account identification device (141) is in theform of a mobile phone, personal digital assistant (PDA), etc. The inputdevice (153) can be used to provide input to the processor (151) tocontrol the operation of the account identification device (141); andthe audio device (157) and the display device (155) may present statusinformation and/or other information, such as advertisements or offers.The account identification device (141) may include further componentsthat are not shown in FIG. 6, such as a cellular communicationssubsystem.

In one embodiment, the communication device (159) may access the accountinformation (142) stored on the memory (167) without going through theprocessor (151).

In one embodiment, the account identification device (141) has fewercomponents than those illustrated in FIG. 6. For example, an accountidentification device (141) does not have the input device (153), theaudio device (157) and the display device (155) in one embodiment; andin another embodiment, an account identification device (141) does nothave components (151-159).

For example, in one embodiment, an account identification device (141)is in the form of a debit card, a credit card, a smartcard, or aconsumer device that has optional features such as magnetic strips, orsmartcards.

An example of an account identification device (141) is a magnetic stripattached to a plastic substrate in the form of a card. The magneticstrip is used as the memory (167) of the account identification device(141) to provide the account information (142). Consumer information,such as account number, expiration date, and consumer name may beprinted or embossed on the card. A semiconductor chip implementing thememory (167) and the communication device (159) may also be embedded inthe plastic card to provide account information (142) in one embodiment.In one embodiment, the account identification device (141) has thesemiconductor chip but not the magnetic strip.

In one embodiment, the account identification device (141) is integratedwith a security device, such as an access card, a radio frequencyidentification (RFID) tag, a security card, a transponder, etc.

In one embodiment, the account identification device (141) is a handheldand compact device. In one embodiment, the account identification device(141) has a size suitable to be placed in a wallet or pocket of theconsumer.

Some examples of an account identification device (141) include a creditcard, a debit card, a stored value device, a payment card, a gift card,a smartcard, a smart media card, a payroll card, a health care card, awrist band, a keychain device, a supermarket discount card, atransponder, and a machine readable medium containing accountinformation (142).

Point of Interaction

In one embodiment, the point of interaction (107) is to provide anadvertisement to the user (101), or to provide information derived fromthe transaction data (109) to the user (101).

In one embodiment, an advertisement is a marketing interaction which mayinclude an announcement and/or an offer of a benefit, such as adiscount, incentive, reward, coupon, gift, cash back, or opportunity(e.g., special ticket/admission). An advertisement may include an offerof a product or service, an announcement of a product or service, or apresentation of a brand of products or services, or a notice of events,facts, opinions, etc. The advertisements can be presented in text,graphics, audio, video, or animation, and as printed matter, webcontent, interactive media, etc. An advertisement may be presented inresponse to the presence of a financial transaction card, or in responseto a financial transaction card being used to make a financialtransaction, or in response to other user activities, such as browsing aweb page, submitting a search request, communicating online, entering awireless communication zone, etc. In one embodiment, the presentation ofadvertisements may be not a result of a user action.

In one embodiment, the point of interaction (107) can be one of variousendpoints of the transaction network, such as point of sale (POS)terminals, automated teller machines (ATMs), electronic kiosks (orcomputer kiosks or interactive kiosks), self-assist checkout terminals,vending machines, gas pumps, websites of banks (e.g., issuer banks oracquirer banks of credit cards), bank statements (e.g., credit cardstatements), websites of the transaction handler (103), websites ofmerchants, checkout websites or web pages for online purchases, etc.

In one embodiment, the point of interaction (107) may be the same as thetransaction terminal (105), such as a point of sale (POS) terminal, anautomated teller machine (ATM), a mobile phone, a computer of the userfor an online transaction, etc. In one embodiment, the point ofinteraction (107) may be co-located with, or near, the transactionterminal (105) (e.g., a video monitor or display, a digital sign), orproduced by the transaction terminal (e.g., a receipt produced by thetransaction terminal (105)). In one embodiment, the point of interaction(107) may be separate from and not co-located with the transactionterminal (105), such as a mobile phone, a personal digital assistant, apersonal computer of the user, a voice mail box of the user, an emailinbox of the user, a digital sign, etc.

For example, the advertisements can be presented on a portion of mediafor a transaction with the customer, which portion might otherwise beunused and thus referred to as a “white space” herein. A white space canbe on a printed matter (e.g., a receipt printed for the transaction, ora printed credit card statement), on a video display (e.g., a displaymonitor of a POS terminal for a retail transaction, an ATM for cashwithdrawal or money transfer, a personal computer of the customer foronline purchases), or on an audio channel (e.g., an interactive voiceresponse (IVR) system for a transaction over a telephonic device).

In one embodiment, the white space is part of a media channel availableto present a message from the transaction handler (103) in connectionwith the processing of a transaction of the user (101). In oneembodiment, the white space is in a media channel that is used to reportinformation about a transaction of the user (101), such as anauthorization status, a confirmation message, a verification message, auser interface to verify a password for the online use of the accountinformation (142), a monthly statement, an alert or a report, or a webpage provided by the portal (143) to access a loyalty program associatedwith the consumer account (146) or a registration program.

In other embodiments, the advertisements can also be presented via othermedia channels which may not involve a transaction processed by thetransaction handler (103). For example, the advertisements can bepresented on publications or announcements (e.g., newspapers, magazines,books, directories, radio broadcasts, television, digital signage, etc.,which may be in an electronic form, or in a printed or painted form).The advertisements may be presented on paper, on websites, onbillboards, on digital signs, or on audio portals.

In one embodiment, the transaction handler (103) purchases the rights touse the media channels from the owner or operators of the media channelsand uses the media channels as advertisement spaces. For example, whitespaces at a point of interaction (e.g., 107) with customers fortransactions processed by the transaction handler (103) can be used todeliver advertisements relevant to the customers conducting thetransactions; and the advertisement can be selected based at least inpart on the intelligence information derived from the accumulatedtransaction data (109) and/or the context at the point of interaction(107) and/or the transaction terminal (105).

In general, a point of interaction (e.g., 107) may or may not be capableof receiving inputs from the customers, and may or may not co-locatedwith a transaction terminal (e.g., 105) that initiates the transactions.The white spaces for presenting the advertisement on the point ofinteraction (107) may be on a portion of a geographical display space(e.g., on a screen), or on a temporal space (e.g., in an audio stream).

In one embodiment, the point of interaction (107) may be used toprimarily to access services not provided by the transaction handler(103), such as services provided by a search engine, a social networkingwebsite, an online marketplace, a blog, a news site, a televisionprogram provider, a radio station, a satellite, a publisher, etc.

In one embodiment, a consumer device is used as the point of interaction(107), which may be a non-portable consumer device or a portablecomputing device. The consumer device is to provide media content to theuser (101) and may receive input from the user (101).

Examples of non-portable consumer devices include a computer terminal, atelevision set, a personal computer, a set-top box, or the like.Examples of portable consumer devices include a portable computer, acellular phone, a personal digital assistant (PDA), a pager, a securitycard, a wireless terminal, or the like. The consumer device may beimplemented as a data processing system as illustrated in FIG. 7, withmore or fewer components.

In one embodiment, the consumer device includes an accountidentification device (141). For example, a smart card used as anaccount identification device (141) is integrated with a mobile phone,or a personal digital assistant (PDA).

In one embodiment, the point of interaction (107) is integrated with atransaction terminal (105). For example, a self-service checkoutterminal includes a touch pad to interact with the user (101); and anATM machine includes a user interface subsystem to interact with theuser (101).

Hardware

In one embodiment, a computing apparatus is configured to include someof the components of systems illustrated in various figures, such as thetransaction handler (103), the profile generator (121), the mediacontroller (115), the portal (143), the profile selector (129), theadvertisement selector (133), the user tracker (113), the correlator,and their associated storage devices, such as the data warehouse (149).

In one embodiment, at least some of the components such as thetransaction handler (103), the transaction terminal (105), the point ofinteraction (107), the user tracker (113), the media controller (115),the correlator (117), the profile generator (121), the profile selector(129), the advertisement selector (133), the portal (143), the issuerprocessor (145), the acquirer processor (147), and the accountidentification device (141), can be implemented as a computer system,such as a data processing system (170) illustrated in FIG. 7. Some ofthe components may share hardware or be combined on a computer system.In one embodiment, a network of computers can be used to implement oneor more of the components.

Further, the data illustrated in the figures, such as transaction data(109), account data (111), transaction profiles (127), and advertisementdata (135), can be stored in storage devices of one or more computersaccessible to the corresponding components. For example, the transactiondata (109) can be stored in the data warehouse (149) that can beimplemented as a data processing system illustrated in FIG. 7, with moreor fewer components.

In one embodiment, the transaction handler (103) is a payment processingsystem, or a payment card processor, such as a card processor for creditcards, debit cards, etc.

FIG. 7 illustrates a data processing system according to one embodiment.While FIG. 7 illustrates various components of a computer system, it isnot intended to represent any particular architecture or manner ofinterconnecting the components. One embodiment may use other systemsthat have fewer or more components than those shown in FIG. 7.

In FIG. 7, the data processing system (170) includes an inter-connect(171) (e.g., bus and system core logic), which interconnects amicroprocessor(s) (173) and memory (167). The microprocessor (173) iscoupled to cache memory (179) in the example of FIG. 7.

In one embodiment, the inter-connect (171) interconnects themicroprocessor(s) (173) and the memory (167) together and alsointerconnects them to input/output (I/O) device(s) (175) via I/Ocontroller(s) (177). I/O devices (175) may include a display deviceand/or peripheral devices, such as mice, keyboards, modems, networkinterfaces, printers, scanners, video cameras and other devices known inthe art. In one embodiment, when the data processing system is a serversystem, some of the I/O devices (175), such as printers, scanners, mice,and/or keyboards, are optional.

In one embodiment, the inter-connect (171) includes one or more busesconnected to one another through various bridges, controllers and/oradapters. In one embodiment the I/O controllers (177) include a USB(Universal Serial Bus) adapter for controlling USB peripherals, and/oran IEEE-1394 bus adapter for controlling IEEE-1394 peripherals.

In one embodiment, the memory (167) includes one or more of: ROM (ReadOnly Memory), volatile RAM (Random Access Memory), and non-volatilememory, such as hard drive, flash memory, etc.

Volatile RAM is typically implemented as dynamic RAM (DRAM) whichrequires power continually in order to refresh or maintain the data inthe memory. Non-volatile memory is typically a magnetic hard drive, amagnetic optical drive, an optical drive (e.g., a DVD RAM), or othertype of memory system which maintains data even after power is removedfrom the system. The non-volatile memory may also be a random accessmemory.

The non-volatile memory can be a local device coupled directly to therest of the components in the data processing system. A non-volatilememory that is remote from the system, such as a network storage devicecoupled to the data processing system through a network interface suchas a modem or Ethernet interface, can also be used.

In this description, some functions and operations are described asbeing performed by or caused by software code to simplify description.However, such expressions are also used to specify that the functionsresult from execution of the code/instructions by a processor, such as amicroprocessor.

Alternatively, or in combination, the functions and operations asdescribed here can be implemented using special purpose circuitry, withor without software instructions, such as using Application-SpecificIntegrated Circuit (ASIC) or Field-Programmable Gate Array (FPGA).Embodiments can be implemented using hardwired circuitry withoutsoftware instructions, or in combination with software instructions.Thus, the techniques are limited neither to any specific combination ofhardware circuitry and software, nor to any particular source for theinstructions executed by the data processing system.

While one embodiment can be implemented in fully functioning computersand computer systems, various embodiments are capable of beingdistributed as a computing product in a variety of forms and are capableof being applied regardless of the particular type of machine orcomputer-readable media used to actually effect the distribution.

At least some aspects disclosed can be embodied, at least in part, insoftware. That is, the techniques may be carried out in a computersystem or other data processing system in response to its processor,such as a microprocessor, executing sequences of instructions containedin a memory, such as ROM, volatile RAM, non-volatile memory, cache or aremote storage device.

Routines executed to implement the embodiments may be implemented aspart of an operating system or a specific application, component,program, object, module or sequence of instructions referred to as“computer programs.” The computer programs typically include one or moreinstructions set at various times in various memory and storage devicesin a computer, and that, when read and executed by one or moreprocessors in a computer, cause the computer to perform operationsnecessary to execute elements involving the various aspects.

A machine readable medium can be used to store software and data whichwhen executed by a data processing system causes the system to performvarious methods. The executable software and data may be stored invarious places including for example ROM, volatile RAM, non-volatilememory and/or cache. Portions of this software and/or data may be storedin any one of these storage devices. Further, the data and instructionscan be obtained from centralized servers or peer to peer networks.Different portions of the data and instructions can be obtained fromdifferent centralized servers and/or peer to peer networks at differenttimes and in different communication sessions or in a same communicationsession. The data and instructions can be obtained in entirety prior tothe execution of the applications. Alternatively, portions of the dataand instructions can be obtained dynamically, just in time, when neededfor execution. Thus, it is not required that the data and instructionsbe on a machine readable medium in entirety at a particular instance oftime.

Examples of computer-readable media include but are not limited torecordable and non-recordable type media such as volatile andnon-volatile memory devices, read only memory (ROM), random accessmemory (RAM), flash memory devices, floppy and other removable disks,magnetic disk storage media, optical storage media (e.g., Compact DiskRead-Only Memory (CD ROMS), Digital Versatile Disks (DVDs), etc.), amongothers. The computer-readable media may store the instructions.

The instructions may also be embodied in digital and analogcommunication links for electrical, optical, acoustical or other formsof propagated signals, such as carrier waves, infrared signals, digitalsignals, etc. However, propagated signals, such as carrier waves,infrared signals, digital signals, etc. are not tangible machinereadable medium and are not configured to store instructions.

In general, a machine readable medium includes any mechanism thatprovides (i.e., stores and/or transmits) information in a formaccessible by a machine (e.g., a computer, network device, personaldigital assistant, manufacturing tool, any device with a set of one ormore processors, etc.).

In various embodiments, hardwired circuitry may be used in combinationwith software instructions to implement the techniques. Thus, thetechniques are neither limited to any specific combination of hardwarecircuitry and software nor to any particular source for the instructionsexecuted by the data processing system.

Other Aspects

The description and drawings are illustrative and are not to beconstrued as limiting. The present disclosure is illustrative ofinventive features to enable a person skilled in the art to make and usethe techniques. Various features, as described herein, should be used incompliance with all current and future rules, laws and regulationsrelated to privacy, security, permission, consent, authorization, andothers. Numerous specific details are described to provide a thoroughunderstanding. However, in certain instances, well known or conventionaldetails are not described in order to avoid obscuring the description.References to one or an embodiment in the present disclosure are notnecessarily references to the same embodiment; and, such references meanat least one.

The use of headings herein is merely provided for ease of reference, andshall not be interpreted in any way to limit this disclosure or thefollowing claims.

Reference to “one embodiment” or “an embodiment” means that a particularfeature, structure, or characteristic described in connection with theembodiment is included in at least one embodiment of the disclosure. Theappearances of the phrase “in one embodiment” in various places in thespecification are not necessarily all referring to the same embodiment,and are not necessarily all referring to separate or alternativeembodiments mutually exclusive of other embodiments. Moreover, variousfeatures are described which may be exhibited by one embodiment and notby others. Similarly, various requirements are described which may berequirements for one embodiment but not other embodiments. Unlessexcluded by explicit description and/or apparent incompatibility, anycombination of various features described in this description is alsoincluded here. For example, the features described above in connectionwith “in one embodiment” or “in some embodiments” can be all optionallyincluded in one implementation, except where the dependency of certainfeatures on other features, as apparent from the description, may limitthe options of excluding selected features from the implementation, andincompatibility of certain features with other features, as apparentfrom the description, may limit the options of including selectedfeatures together in the implementation.

The disclosures of the above discussed patent documents are herebyincorporated herein by reference.

In the foregoing specification, the disclosure has been described withreference to specific exemplary embodiments thereof. It will be evidentthat various modifications may be made thereto without departing fromthe broader spirit and scope as set forth in the following claims. Thespecification and drawings are, accordingly, to be regarded in anillustrative sense rather than a restrictive sense.

What is claimed is:
 1. A computer-implemented method, comprising:receiving, by a computing apparatus, information of a route of a user;selecting, by the computing apparatus, for location identification, anarea within a predetermined threshold distance from the route;obtaining, by the computing apparatus, locations that are located withinthe area along the route; selecting, by the computing apparatus, atleast a portion of the route for measuring distances of the locationsalong the route; determining, by the computing apparatus, respectivedistances of the locations from the selected portion; obtaining, by thecomputing apparatus, affinity values of the user for the locations;determining, by the computing apparatus, respective utility values forthe locations based on the affinity values of the user for the locationsand the respective distances of the locations from the selectedportions; ranking, by the computing apparatus, the locations based onthe respective utility values; and presenting, by the computingapparatus, one or more top ranked locations having highest utilityvalues to the user.
 2. A computer-implemented method, comprising:identifying, by a computing apparatus, a plurality of locations;computing, by the computing apparatus, respective distances from thelocations to a route of predicted travel of a user; computing, by thecomputing apparatus, affinity values of the user for the locations basedon electronic payment transaction data of the user; computing respectiveutility values of the locations from a predetermined function based onthe affinity values of the user for the locations and the respectivedistances of the locations, wherein each utility value of a location isthe predetermined function of an affinity value of the user for thelocation, and a distance of the location to the route, where the utilityvalue of the location computed from the predetermined function increaseswhen the affinity value of the user for the location increases, and whenthe distance of the location decreases; ranking, by the computingapparatus, the locations based on the respective utility values;selecting, by the computing apparatus, one or more top ranked locationsfrom the plurality of locations based on the utility values; andpresenting, by the computing apparatus to the user, the one or moreselected locations.
 3. The computer-implemented method of claim 2,wherein the respective distances from the locations are computed to anorigin of the route.
 4. The computer-implemented method of claim 2,wherein the respective distances from the locations are computed to adestination of the route.
 5. The computer-implemented method of claim 2,wherein the respective distances from the locations are computed torespective segments of the route that are closest to the locationsrespectively.
 6. The computer-implemented method of claim 2, furthercomprising: determining, by the computing apparatus and based on acurrent location of the user along the route, a portion of the routefrom which the the respective distances from the locations are computed.7. The computer implemented method of claim 2, wherein the utility valueof the location is proportional to the affinity value of the user forthe location.
 8. The computer implemented method of claim 2, wherein theutility value of the location is inverse proportional to the distance ofthe location to the route.
 9. The computer implement method of claim 2,wherein the utility value of the location is proportional to theaffinity value of the user for the location divided by the distance ofthe location to the route.
 10. The computer-implemented method of claim2, wherein the affinity values are retrieved from profile information ofthe user.
 11. A computing apparatus, comprising: at least onemicroprocessor; and memory storing instructions configured to instructthe at least one microprocessor to at least: receive, in the computingapparatus, information of routes traversed by a user; identify, by thecomputing apparatus, locations on the routes; organize, by the computingapparatus, the routes as a set of route words in a route dictionary,wherein each route word identifies one of the routes using a sequence ofsymbols, each of the symbols in the route words uniquely identifies oneof the locations, each of the locations is uniquely identified by one ofthe symbols in the route words; store, by the computing apparatus,information identifying travels of the user along the routes inassociation with the route words in the route dictionary, includingfrequency of travel along routes as term frequency of the route words;predict, by the computing apparatus, a remaining portion of a currentroute of a user from the route dictionary based on one or more locationson a traversed portion the current route that have been traversed by theuser; identify, by the computing apparatus, a plurality of points ofinterest; compute, by the computing apparatus, distances from the pointsof interests to the current route; compute, by the computing apparatus,affinity values of the user for the points of interests base onelectronic payment transaction data of the user; combine, by thecomputing apparatus, the distances and the affinity values to generateutility values of the points of interests; select, by the computingapparatus, top points of interests based on the utility values; andpresent, by the computing apparatus, the top points of interests to theuser while the user is traversing the current route.
 12. The computingapparatus of claim 11, wherein the distances from the points ofinterests are computed to the remaining portion of the current route.13. The computing apparatus of claim 11, wherein the distances from thepoints of interests are computed to an origin or destination of thecurrent route.
 14. The computing apparatus of claim 11, wherein thedistances from the points of interests are computed to respectivesegments of the route that are closest to the locations respectively.15. The computing apparatus of claim 11, wherein the utility values foreach of the locations is a product of the affinity value and therespective distance of each location from the selected portion.
 16. Anon-transitory computer storage medium storing instructions configuredto instruct a computing apparatus to perform a method, the methodcomprising: identifying, by a computing apparatus, a route to betraversed by a user; identifying, by the computing apparatus, aplurality of points of interest; determining, by the computingapparatus, lengths of line segments of the route; computing, by thecomputing apparatus, threshold lengths for a set of vertexes of theroute as combination of the lengths of the line segments of the routeand a threshold value; dividing, by the computing apparatus, theplurality of points of interest into a first subset of points ofinterest within an area around the route, wherein each of the firstsubset of points of interest has a distance to a vertex among thevertexes of the route less than a respective threshold length associatedwith the vertex; and a second subset of points of interest outside anarea around the route, wherein each of the second subset of points ofinterest has distances to the vertexes of the route greater respectivethreshold lengths associated with the vertexes; computing, by thecomputing apparatus, distances from the first subset of points ofinterests to the route; computing, by the computing apparatus, affinityvalues of the user for the first subset of points of interests base onelectronic payment transaction data of the user; combining, by thecomputing apparatus, the distances and the affinity values to generateutility values of the first subset of points of interests; selecting, bythe computing apparatus, top points of interests based on the utilityvalues; and communicating, by the computing apparatus, the top points ofinterests to the user.
 17. The non-transitory computer storage medium ofclaim 16, wherein the distances from the first subset of points ofinterests are computed to an origin or destination of the route.
 18. Thenon-transitory computer storage medium of claim 16, wherein thedistances from the first subset of points of interests are computed torespective route segments of the route that are closest to the locationsrespectively.
 19. The non-transitory computer storage medium of claim16, wherein the utility values are proportional to the affinity valuesand inverse proportional to the distances.
 20. The non-transitorycomputer storage medium of claim 16, wherein the threshold lengths are apredetermined function of: the lengths of the line segments of theroute; and the threshold value.